diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py
index 229457a73b4..fb55a7f2448 100644
--- a/litellm/llms/bedrock/chat/converse_transformation.py
+++ b/litellm/llms/bedrock/chat/converse_transformation.py
@@ -91,34 +91,6 @@
     "compact-2026-01-12",  # The compact beta feature is not currently supported on the Converse and ConverseStream APIs
 ]
 
-# Models that support Bedrock's native structured outputs API (outputConfig.textFormat)
-# Uses substring matching against the Bedrock model ID
-# Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/structured-output.html
-BEDROCK_NATIVE_STRUCTURED_OUTPUT_MODELS = {
-    # Anthropic Claude 4.5+
-    "claude-haiku-4-5",
-    "claude-sonnet-4-5",
-    "claude-opus-4-5",
-    "claude-opus-4-6",
-    # Qwen3
-    "qwen3",
-    # DeepSeek
-    "deepseek-v3.1",
-    # Gemma 3
-    "gemma-3",
-    # MiniMax
-    "minimax-m2",
-    # Mistral (magistral-small excluded: broken constrained decoding on Bedrock)
-    "ministral",
-    "mistral-large-3",
-    "voxtral",
-    # Moonshot
-    "kimi-k2",
-    # NVIDIA
-    "nemotron-nano",
-    # OpenAI (gpt-oss excluded: broken constrained decoding, works via tool-call fallback)
-}
-
 
 class AmazonConverseConfig(BaseConfig):
     """
@@ -188,8 +160,7 @@ def _convert_consecutive_user_messages_to_guarded_text(
 
             if isinstance(content, list):
                 has_guarded_text = any(
-                    isinstance(item, dict) and item.get("type") == "guarded_text"
-                    for item in content
+                    isinstance(item, dict) and item.get("type") == "guarded_text" for item in content
                 )
                 if has_guarded_text:
                     continue  # Skip this message if it already has guarded_text
@@ -350,13 +321,9 @@ def _is_nova_2_model(self, model: str) -> bool:
 
         # Check if the model is a Nova 2 model (matches nova-2-lite, nova-2-pro, etc.)
         # Also check for nova-2/ spec prefix for imported models
-        return model_without_region.startswith(
-            "amazon.nova-2-"
-        ) or model_without_region.startswith("nova-2/")
+        return model_without_region.startswith("amazon.nova-2-") or model_without_region.startswith("nova-2/")
 
-    def _map_web_search_options(
-        self, web_search_options: dict, model: str
-    ) -> Optional[BedrockToolBlock]:
+    def _map_web_search_options(self, web_search_options: dict, model: str) -> Optional[BedrockToolBlock]:
         """
         Map web_search_options to Nova grounding systemTool.
 
@@ -385,9 +352,7 @@ def _map_web_search_options(
         # (unlike Anthropic), so we just enable grounding with no options
         return BedrockToolBlock(systemTool={"name": "nova_grounding"})
 
-    def _transform_reasoning_effort_to_reasoning_config(
-        self, reasoning_effort: str
-    ) -> dict:
+    def _transform_reasoning_effort_to_reasoning_config(self, reasoning_effort: str) -> dict:
         """
         Transform reasoning_effort parameter to Nova 2 reasoningConfig structure.
 
@@ -432,9 +397,7 @@ def _transform_reasoning_effort_to_reasoning_config(
             }
         }
 
-    def _handle_reasoning_effort_parameter(
-        self, model: str, reasoning_effort: str, optional_params: dict
-    ) -> None:
+    def _handle_reasoning_effort_parameter(self, model: str, reasoning_effort: str, optional_params: dict) -> None:
         """
         Handle the reasoning_effort parameter based on the model type.
 
@@ -471,9 +434,7 @@ def _handle_reasoning_effort_parameter(
             optional_params["reasoning_effort"] = reasoning_effort
         elif self._is_nova_2_model(model):
             # Nova 2 models: transform to reasoningConfig
-            reasoning_config = self._transform_reasoning_effort_to_reasoning_config(
-                reasoning_effort
-            )
+            reasoning_config = self._transform_reasoning_effort_to_reasoning_config(reasoning_effort)
             optional_params.update(reasoning_config)
         else:
             # Anthropic and other models: convert to thinking parameter
@@ -493,8 +454,7 @@ def _clamp_thinking_budget_tokens(optional_params: dict) -> None:
             budget = thinking.get("budget_tokens")
             if isinstance(budget, int) and budget < BEDROCK_MIN_THINKING_BUDGET_TOKENS:
                 verbose_logger.debug(
-                    "Bedrock requires thinking.budget_tokens >= %d, got %d. "
-                    "Clamping to minimum.",
+                    "Bedrock requires thinking.budget_tokens >= %d, got %d. Clamping to minimum.",
                     BEDROCK_MIN_THINKING_BUDGET_TOKENS,
                     budget,
                 )
@@ -518,9 +478,7 @@ def get_supported_openai_params(self, model: str) -> List[str]:
             "parallel_tool_calls",
         ]
 
-        if (
-            "arn" in model
-        ):  # we can't infer the model from the arn, so just add all params
+        if "arn" in model:  # we can't infer the model from the arn, so just add all params
             supported_params.append("tools")
             supported_params.append("tool_choice")
             supported_params.append("thinking")
@@ -542,9 +500,7 @@ def get_supported_openai_params(self, model: str) -> List[str]:
             or base_model.startswith("meta.llama3-3")
             or base_model.startswith("meta.llama4")
             or base_model.startswith("amazon.nova")
-            or supports_function_calling(
-                model=model, custom_llm_provider=self.custom_llm_provider
-            )
+            or supports_function_calling(model=model, custom_llm_provider=self.custom_llm_provider)
         ):
             supported_params.append("tools")
 
@@ -554,9 +510,7 @@ def get_supported_openai_params(self, model: str) -> List[str]:
 
         if litellm.utils.supports_tool_choice(
             model=model, custom_llm_provider=self.custom_llm_provider
-        ) or litellm.utils.supports_tool_choice(
-            model=base_model, custom_llm_provider=self.custom_llm_provider
-        ):
+        ) or litellm.utils.supports_tool_choice(model=base_model, custom_llm_provider=self.custom_llm_provider):
             # only anthropic and mistral support tool choice config. otherwise (E.g. cohere) will fail the call - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
             supported_params.append("tool_choice")
 
@@ -575,9 +529,7 @@ def get_supported_openai_params(self, model: str) -> List[str]:
                 model=model,
                 custom_llm_provider=self.custom_llm_provider,
             )
-            or supports_reasoning(
-                model=base_model, custom_llm_provider=self.custom_llm_provider
-            )
+            or supports_reasoning(model=base_model, custom_llm_provider=self.custom_llm_provider)
         ):
             supported_params.append("thinking")
             supported_params.append("reasoning_effort")
@@ -602,9 +554,7 @@ def map_tool_choice_values(
             return ToolChoiceValuesBlock(auto={})
         elif isinstance(tool_choice, dict):
             # only supported for anthropic + mistral models - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
-            specific_tool = SpecificToolChoiceBlock(
-                name=tool_choice.get("function", {}).get("name", "")
-            )
+            specific_tool = SpecificToolChoiceBlock(name=tool_choice.get("function", {}).get("name", ""))
             return ToolChoiceValuesBlock(tool=specific_tool)
         else:
             raise litellm.utils.UnsupportedParamsError(
@@ -624,15 +574,9 @@ def get_supported_video_types(self) -> List[str]:
         return ["mp4", "mov", "mkv", "webm", "flv", "mpeg", "mpg", "wmv", "3gp"]
 
     def get_all_supported_content_types(self) -> List[str]:
-        return (
-            self.get_supported_image_types()
-            + self.get_supported_document_types()
-            + self.get_supported_video_types()
-        )
+        return self.get_supported_image_types() + self.get_supported_document_types() + self.get_supported_video_types()
 
-    def is_computer_use_tool_used(
-        self, tools: Optional[List[OpenAIChatCompletionToolParam]], model: str
-    ) -> bool:
+    def is_computer_use_tool_used(self, tools: Optional[List[OpenAIChatCompletionToolParam]], model: str) -> bool:
         """Check if computer use tools are being used in the request."""
         if tools is None:
             return False
@@ -645,9 +589,7 @@ def is_computer_use_tool_used(
                         return True
         return False
 
-    def _transform_computer_use_tools(
-        self, computer_use_tools: List[OpenAIChatCompletionToolParam]
-    ) -> List[dict]:
+    def _transform_computer_use_tools(self, computer_use_tools: List[OpenAIChatCompletionToolParam]) -> List[dict]:
         """Transform computer use tools to Bedrock format."""
         transformed_tools: List[dict] = []
 
@@ -689,9 +631,7 @@ def _transform_computer_use_tools(
 
     def _separate_computer_use_tools(
         self, tools: List[OpenAIChatCompletionToolParam], model: str
-    ) -> Tuple[
-        List[OpenAIChatCompletionToolParam], List[OpenAIChatCompletionToolParam]
-    ]:
+    ) -> Tuple[List[OpenAIChatCompletionToolParam], List[OpenAIChatCompletionToolParam]]:
         """
         Separate computer use tools from regular function tools.
 
@@ -764,10 +704,27 @@ def _create_json_tool_call_for_response_format(
 
     @staticmethod
     def _supports_native_structured_outputs(model: str) -> bool:
-        """Check if the Bedrock model supports native structured outputs (outputConfig.textFormat)."""
-        return any(
-            substring in model for substring in BEDROCK_NATIVE_STRUCTURED_OUTPUT_MODELS
-        )
+        """Check if the Bedrock model supports native structured outputs (outputConfig.textFormat).
+
+        Looks up the ``supports_native_structured_output`` flag in
+        ``litellm.model_cost`` (set in the cost JSON).
+        Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/structured-output.html
+        """
+        from litellm.llms.bedrock.common_utils import get_bedrock_base_model
+
+        base_model = get_bedrock_base_model(model)
+
+        # Try direct lookup
+        info = litellm.model_cost.get(base_model)
+
+        # Try without version suffix (e.g. "model-v1:0" -> "model-v1")
+        if info is None and ":" in base_model:
+            info = litellm.model_cost.get(base_model.rsplit(":", 1)[0])
+
+        if info is not None:
+            return info.get("supports_native_structured_output", False) is True
+
+        return False
 
     @staticmethod
     def _add_additional_properties_to_schema(schema: dict) -> dict:
@@ -790,25 +747,18 @@ def _add_additional_properties_to_schema(schema: dict) -> dict:
         # Recurse into nested schemas
         if "properties" in result and isinstance(result["properties"], dict):
             result["properties"] = {
-                k: AmazonConverseConfig._add_additional_properties_to_schema(v)
-                for k, v in result["properties"].items()
+                k: AmazonConverseConfig._add_additional_properties_to_schema(v) for k, v in result["properties"].items()
             }
         if "items" in result and isinstance(result["items"], dict):
-            result["items"] = AmazonConverseConfig._add_additional_properties_to_schema(
-                result["items"]
-            )
+            result["items"] = AmazonConverseConfig._add_additional_properties_to_schema(result["items"])
         for defs_key in ("$defs", "definitions"):
             if defs_key in result and isinstance(result[defs_key], dict):
                 result[defs_key] = {
-                    k: AmazonConverseConfig._add_additional_properties_to_schema(v)
-                    for k, v in result[defs_key].items()
+                    k: AmazonConverseConfig._add_additional_properties_to_schema(v) for k, v in result[defs_key].items()
                 }
         for key in ("anyOf", "allOf", "oneOf"):
             if key in result and isinstance(result[key], list):
-                result[key] = [
-                    AmazonConverseConfig._add_additional_properties_to_schema(item)
-                    for item in result[key]
-                ]
+                result[key] = [AmazonConverseConfig._add_additional_properties_to_schema(item) for item in result[key]]
 
         return result
 
@@ -838,9 +788,7 @@ def _create_output_config_for_response_format(
         }
         """
         if json_schema is not None:
-            json_schema = AmazonConverseConfig._add_additional_properties_to_schema(
-                json_schema
-            )
+            json_schema = AmazonConverseConfig._add_additional_properties_to_schema(json_schema)
         schema_str = json.dumps(json_schema) if json_schema is not None else "{}"
         json_schema_def: JsonSchemaDefinition = {"schema": schema_str}
         if name is not None:
@@ -862,14 +810,9 @@ def _apply_tool_call_transformation(
         non_default_params: dict,
         optional_params: dict,
     ):
-        optional_params = self._add_tools_to_optional_params(
-            optional_params=optional_params, tools=tools
-        )
+        optional_params = self._add_tools_to_optional_params(optional_params=optional_params, tools=tools)
 
-        if (
-            "meta.llama3-3-70b-instruct-v1:0" in model
-            and non_default_params.get("stream", False) is True
-        ):
+        if "meta.llama3-3-70b-instruct-v1:0" in model and non_default_params.get("stream", False) is True:
             optional_params["fake_stream"] = True
 
     def map_openai_params(
@@ -913,7 +856,9 @@ def map_openai_params(
                 )
             if param == "tool_choice":
                 _tool_choice_value = self.map_tool_choice_values(
-                    model=model, tool_choice=value, drop_params=drop_params  # type: ignore
+                    model=model,
+                    tool_choice=value,
+                    drop_params=drop_params,  # type: ignore
                 )
                 if _tool_choice_value is not None:
                     optional_params["tool_choice"] = _tool_choice_value
@@ -1024,14 +969,10 @@ def _translate_response_format_param(
                 json_schema=json_schema,
                 description=description,
             )
-            optional_params = self._add_tools_to_optional_params(
-                optional_params=optional_params, tools=[_tool]
-            )
+            optional_params = self._add_tools_to_optional_params(optional_params=optional_params, tools=[_tool])
 
             if (
-                litellm.utils.supports_tool_choice(
-                    model=model, custom_llm_provider=self.custom_llm_provider
-                )
+                litellm.utils.supports_tool_choice(model=model, custom_llm_provider=self.custom_llm_provider)
                 and not is_thinking_enabled
             ):
                 optional_params["tool_choice"] = ToolChoiceValuesBlock(
@@ -1043,9 +984,7 @@ def _translate_response_format_param(
         optional_params["json_mode"] = True
         return optional_params
 
-    def update_optional_params_with_thinking_tokens(
-        self, non_default_params: dict, optional_params: dict
-    ):
+    def update_optional_params_with_thinking_tokens(self, non_default_params: dict, optional_params: dict):
         """
         Handles scenario where max tokens is not specified. For anthropic models (anthropic api/bedrock/vertex ai), this requires having the max tokens being set and being greater than the thinking token budget.
 
@@ -1063,13 +1002,9 @@ def update_optional_params_with_thinking_tokens(
         is_thinking_enabled = self.is_thinking_enabled(optional_params)
         is_max_tokens_in_request = self.is_max_tokens_in_request(non_default_params)
         if is_thinking_enabled and not is_max_tokens_in_request:
-            thinking_token_budget = cast(dict, optional_params["thinking"]).get(
-                "budget_tokens", None
-            )
+            thinking_token_budget = cast(dict, optional_params["thinking"]).get("budget_tokens", None)
             if thinking_token_budget is not None:
-                optional_params["maxTokens"] = (
-                    thinking_token_budget + DEFAULT_MAX_TOKENS
-                )
+                optional_params["maxTokens"] = thinking_token_budget + DEFAULT_MAX_TOKENS
 
     @overload
     def _get_cache_point_block(
@@ -1135,23 +1070,15 @@ def _transform_system_message(
             if message["role"] == "system":
                 system_prompt_indices.append(idx)
                 if isinstance(message["content"], str) and message["content"]:
-                    system_content_blocks.append(
-                        SystemContentBlock(text=message["content"])
-                    )
-                    cache_block = self._get_cache_point_block(
-                        message, block_type="system", model=model
-                    )
+                    system_content_blocks.append(SystemContentBlock(text=message["content"]))
+                    cache_block = self._get_cache_point_block(message, block_type="system", model=model)
                     if cache_block:
                         system_content_blocks.append(cache_block)
                 elif isinstance(message["content"], list):
                     for m in message["content"]:
                         if m.get("type") == "text" and m.get("text"):
-                            system_content_blocks.append(
-                                SystemContentBlock(text=m["text"])
-                            )
-                            cache_block = self._get_cache_point_block(
-                                m, block_type="system", model=model
-                            )
+                            system_content_blocks.append(SystemContentBlock(text=m["text"]))
+                            cache_block = self._get_cache_point_block(m, block_type="system", model=model)
                             if cache_block:
                                 system_content_blocks.append(cache_block)
         if len(system_prompt_indices) > 0:
@@ -1189,16 +1116,10 @@ def _prepare_request_params(
         # Exceptions should not be stored in optional_params (this is a defensive fix)
         cleaned_params = filter_exceptions_from_params(optional_params)
         inference_params = safe_deep_copy(cleaned_params)
-        supported_converse_params = list(
-            AmazonConverseConfig.__annotations__.keys()
-        ) + ["top_k"]
+        supported_converse_params = list(AmazonConverseConfig.__annotations__.keys()) + ["top_k"]
         supported_tool_call_params = ["tools", "tool_choice"]
         supported_config_params = list(self.get_config_blocks().keys())
-        total_supported_params = (
-            supported_converse_params
-            + supported_tool_call_params
-            + supported_config_params
-        )
+        total_supported_params = supported_converse_params + supported_tool_call_params + supported_config_params
         inference_params.pop("json_mode", None)  # used for handling json_schema
         # Anthropic-only key. Bedrock expects `outputConfig` (camelCase) and
         # will reject `output_config` if it leaks through pass-through routes.
@@ -1209,25 +1130,15 @@ def _prepare_request_params(
         if request_metadata is not None:
             self._validate_request_metadata(request_metadata)
 
-        output_config: Optional[OutputConfigBlock] = inference_params.pop(
-            "outputConfig", None
-        )
-        inference_params.pop(
-            "output_config", None
-        )  # Bedrock Converse doesn't support it
+        output_config: Optional[OutputConfigBlock] = inference_params.pop("outputConfig", None)
+        inference_params.pop("output_config", None)  # Bedrock Converse doesn't support it
 
         # keep supported params in 'inference_params', and set all model-specific params in 'additional_request_params'
-        additional_request_params = {
-            k: v for k, v in inference_params.items() if k not in total_supported_params
-        }
-        inference_params = {
-            k: v for k, v in inference_params.items() if k in total_supported_params
-        }
+        additional_request_params = {k: v for k, v in inference_params.items() if k not in total_supported_params}
+        inference_params = {k: v for k, v in inference_params.items() if k in total_supported_params}
 
         # Handle parallel_tool_calls configuration
-        parallel_tool_use_config = additional_request_params.pop(
-            "_parallel_tool_use_config", None
-        )
+        parallel_tool_use_config = additional_request_params.pop("_parallel_tool_use_config", None)
         if parallel_tool_use_config is not None and is_claude_4_5_on_bedrock(model):
             for key, value in parallel_tool_use_config.items():
                 if (
@@ -1242,9 +1153,7 @@ def _prepare_request_params(
         additional_request_params.pop("parallel_tool_calls", None)
 
         # Only set the topK value in for models that support it
-        additional_request_params.update(
-            self._handle_top_k_value(model, inference_params)
-        )
+        additional_request_params.update(self._handle_top_k_value(model, inference_params))
 
         # Filter out internal/MCP-related parameters that shouldn't be sent to the API
         # These are LiteLLM internal parameters, not API parameters
@@ -1253,9 +1162,7 @@ def _prepare_request_params(
         # Filter out non-serializable objects (exceptions, callables, logging objects, etc.)
         # from additional_request_params to prevent JSON serialization errors
         # This filters: Exception objects, callable objects (functions), Logging objects, etc.
-        additional_request_params = filter_exceptions_from_params(
-            additional_request_params
-        )
+        additional_request_params = filter_exceptions_from_params(additional_request_params)
 
         return (
             inference_params,
@@ -1302,9 +1209,7 @@ def _process_tools_and_beta(
         # Only separate tools if computer use tools are actually present
         if filtered_tools and self.is_computer_use_tool_used(filtered_tools, model):
             # Separate computer use tools from regular function tools
-            computer_use_tools, regular_tools = self._separate_computer_use_tools(
-                filtered_tools, model
-            )
+            computer_use_tools, regular_tools = self._separate_computer_use_tools(filtered_tools, model)
 
             # Process regular function tools using existing logic
             bedrock_tools = _bedrock_tools_pt(regular_tools)
@@ -1365,9 +1270,7 @@ def _process_tools_and_beta(
 
                 anthropic_beta_list.append(computer_use_header)
                 # Transform computer use tools to proper Bedrock format
-                transformed_computer_tools = self._transform_computer_use_tools(
-                    computer_use_tools
-                )
+                transformed_computer_tools = self._transform_computer_use_tools(computer_use_tools)
                 additional_request_params["tools"] = transformed_computer_tools
         else:
             # No computer use tools, process all tools as regular tools
@@ -1396,15 +1299,9 @@ def _transform_request_helper(
         """
         Bedrock doesn't support tool calling without `tools=` param specified.
         """
-        if (
-            "tools" not in optional_params
-            and messages is not None
-            and has_tool_call_blocks(messages)
-        ):
+        if "tools" not in optional_params and messages is not None and has_tool_call_blocks(messages):
             if litellm.modify_params:
-                optional_params["tools"] = add_dummy_tool(
-                    custom_llm_provider="bedrock_converse"
-                )
+                optional_params["tools"] = add_dummy_tool(custom_llm_provider="bedrock_converse")
             else:
                 raise litellm.UnsupportedParamsError(
                     message="Bedrock doesn't support tool calling without `tools=` param specified. Pass `tools=` param OR set `litellm.modify_params = True` // `litellm_settings::modify_params: True` to add dummy tool to the request.",
@@ -1448,9 +1345,7 @@ def _transform_request_helper(
 
         bedrock_tool_config: Optional[ToolConfigBlock] = None
         if len(bedrock_tools) > 0:
-            tool_choice_values: ToolChoiceValuesBlock = inference_params.pop(
-                "tool_choice", None
-            )
+            tool_choice_values: ToolChoiceValuesBlock = inference_params.pop("tool_choice", None)
             bedrock_tool_config = ToolConfigBlock(
                 tools=bedrock_tools,
             )
@@ -1460,9 +1355,7 @@ def _transform_request_helper(
         data: CommonRequestObject = {
             "additionalModelRequestFields": additional_request_params,
             "system": system_content_blocks,
-            "inferenceConfig": self._transform_inference_params(
-                inference_params=inference_params
-            ),
+            "inferenceConfig": self._transform_inference_params(inference_params=inference_params),
         }
 
         # Handle all config blocks
@@ -1492,14 +1385,10 @@ async def _async_transform_request(
         litellm_params: dict,
         headers: Optional[dict] = None,
     ) -> RequestObject:
-        messages, system_content_blocks = self._transform_system_message(
-            messages, model=model
-        )
+        messages, system_content_blocks = self._transform_system_message(messages, model=model)
 
         # Convert last user message to guarded_text if guardrailConfig is present
-        messages = self._convert_consecutive_user_messages_to_guarded_text(
-            messages, optional_params
-        )
+        messages = self._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
         ## TRANSFORMATION ##
 
         _data: CommonRequestObject = self._transform_request_helper(
@@ -1510,13 +1399,11 @@ async def _async_transform_request(
             headers=headers,
         )
 
-        bedrock_messages = (
-            await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-                messages=messages,
-                model=model,
-                llm_provider="bedrock_converse",
-                user_continue_message=litellm_params.pop("user_continue_message", None),
-            )
+        bedrock_messages = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model=model,
+            llm_provider="bedrock_converse",
+            user_continue_message=litellm_params.pop("user_continue_message", None),
         )
 
         data: RequestObject = {"messages": bedrock_messages, **_data}
@@ -1550,14 +1437,10 @@ def _transform_request(
         litellm_params: dict,
         headers: Optional[dict] = None,
     ) -> RequestObject:
-        messages, system_content_blocks = self._transform_system_message(
-            messages, model=model
-        )
+        messages, system_content_blocks = self._transform_system_message(messages, model=model)
 
         # Convert last user message to guarded_text if guardrailConfig is present
-        messages = self._convert_consecutive_user_messages_to_guarded_text(
-            messages, optional_params
-        )
+        messages = self._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
 
         _data: CommonRequestObject = self._transform_request_helper(
             model=model,
@@ -1606,9 +1489,7 @@ def transform_response(
             encoding=encoding,
         )
 
-    def _transform_reasoning_content(
-        self, reasoning_content_blocks: List[BedrockConverseReasoningContentBlock]
-    ) -> str:
+    def _transform_reasoning_content(self, reasoning_content_blocks: List[BedrockConverseReasoningContentBlock]) -> str:
         """
         Extract the reasoning text from the reasoning content blocks
 
@@ -1624,9 +1505,7 @@ def _transform_thinking_blocks(
         self, thinking_blocks: List[BedrockConverseReasoningContentBlock]
     ) -> List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]]:
         """Return a consistent format for thinking blocks between Anthropic and Bedrock."""
-        thinking_blocks_list: List[
-            Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
-        ] = []
+        thinking_blocks_list: List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]] = []
         for block in thinking_blocks:
             if "reasoningText" in block:
                 _thinking_block = ChatCompletionThinkingBlock(type="thinking")
@@ -1662,21 +1541,11 @@ def _transform_usage(
             cache_creation_input_tokens = usage["cacheWriteInputTokens"]
             input_tokens += cache_creation_input_tokens
 
-        prompt_tokens_details = PromptTokensDetailsWrapper(
-            cached_tokens=cache_read_input_tokens
-        )
-        reasoning_tokens = (
-            token_counter(text=reasoning_content, count_response_tokens=True)
-            if reasoning_content
-            else 0
-        )
+        prompt_tokens_details = PromptTokensDetailsWrapper(cached_tokens=cache_read_input_tokens)
+        reasoning_tokens = token_counter(text=reasoning_content, count_response_tokens=True) if reasoning_content else 0
         completion_tokens_details = CompletionTokensDetailsWrapper(
             reasoning_tokens=reasoning_tokens,
-            text_tokens=(
-                output_tokens - reasoning_tokens
-                if reasoning_tokens > 0
-                else output_tokens
-            ),
+            text_tokens=(output_tokens - reasoning_tokens if reasoning_tokens > 0 else output_tokens),
         )
         openai_usage = Usage(
             prompt_tokens=input_tokens,
@@ -1691,9 +1560,7 @@ def _transform_usage(
 
     def get_tool_call_names(
         self,
-        tools: Optional[
-            Union[List[ToolBlock], List[OpenAIChatCompletionToolParam]]
-        ] = None,
+        tools: Optional[Union[List[ToolBlock], List[OpenAIChatCompletionToolParam]]] = None,
     ) -> List[str]:
         if tools is None:
             return []
@@ -1732,13 +1599,8 @@ def apply_tool_call_transformation_if_needed(
             try:
                 tool_call_names = self.get_tool_call_names(tools)
                 json_content = json.loads(message.content)
-                if (
-                    json_content.get("type") == "function"
-                    and json_content.get("name") in tool_call_names
-                ):
-                    tool_calls = [
-                        ChatCompletionMessageToolCall(function=Function(**json_content))
-                    ]
+                if json_content.get("type") == "function" and json_content.get("name") in tool_call_names:
+                    tool_calls = [ChatCompletionMessageToolCall(function=Function(**json_content))]
 
                     message.tool_calls = tool_calls
                     message.content = None
@@ -1767,9 +1629,7 @@ def _translate_message_content(
         """
         content_str = ""
         tools: List[ChatCompletionToolCallChunk] = []
-        reasoningContentBlocks: Optional[
-            List[BedrockConverseReasoningContentBlock]
-        ] = None
+        reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = None
         citationsContentBlocks: Optional[List[CitationsContentBlock]] = None
         for idx, content in enumerate(content_blocks):
             """
@@ -1786,9 +1646,7 @@ def _translate_message_content(
             if "toolUse" in content:
                 ## check tool name was formatted by litellm
                 _response_tool_name = content["toolUse"]["name"]
-                response_tool_name = get_bedrock_tool_name(
-                    response_tool_name=_response_tool_name
-                )
+                response_tool_name = get_bedrock_tool_name(response_tool_name=_response_tool_name)
                 _function_chunk = ChatCompletionToolCallFunctionChunk(
                     name=response_tool_name,
                     arguments=json.dumps(content["toolUse"]["input"]),
@@ -1839,11 +1697,7 @@ def _unwrap_bedrock_properties(json_str: str) -> str:
         """
         try:
             response_data = json.loads(json_str)
-            if (
-                isinstance(response_data, dict)
-                and "properties" in response_data
-                and len(response_data) == 1
-            ):
+            if isinstance(response_data, dict) and "properties" in response_data and len(response_data) == 1:
                 response_data = response_data["properties"]
                 return json.dumps(response_data)
         except json.JSONDecodeError:
@@ -1867,11 +1721,7 @@ def _filter_json_mode_tools(
         if not json_mode or not tools:
             return tools if tools else None
 
-        json_tool_indices = [
-            i
-            for i, t in enumerate(tools)
-            if t["function"].get("name") == RESPONSE_FORMAT_TOOL_NAME
-        ]
+        json_tool_indices = [i for i, t in enumerate(tools) if t["function"].get("name") == RESPONSE_FORMAT_TOOL_NAME]
 
         if not json_tool_indices:
             # No json_tool_call found, return tools unchanged
@@ -1879,14 +1729,10 @@ def _filter_json_mode_tools(
 
         if len(json_tool_indices) == len(tools):
             # All tools are json_tool_call — convert first one to content
-            verbose_logger.debug(
-                "Processing JSON tool call response for response_format"
-            )
+            verbose_logger.debug("Processing JSON tool call response for response_format")
             json_mode_content_str: Optional[str] = tools[0]["function"].get("arguments")
             if json_mode_content_str is not None:
-                json_mode_content_str = AmazonConverseConfig._unwrap_bedrock_properties(
-                    json_mode_content_str
-                )
+                json_mode_content_str = AmazonConverseConfig._unwrap_bedrock_properties(json_mode_content_str)
                 chat_completion_message["content"] = json_mode_content_str
             return None
 
@@ -1896,13 +1742,9 @@ def _filter_json_mode_tools(
         first_idx = json_tool_indices[0]
         json_mode_args = tools[first_idx]["function"].get("arguments")
         if json_mode_args is not None:
-            json_mode_args = AmazonConverseConfig._unwrap_bedrock_properties(
-                json_mode_args
-            )
+            json_mode_args = AmazonConverseConfig._unwrap_bedrock_properties(json_mode_args)
             existing = chat_completion_message.get("content") or ""
-            chat_completion_message["content"] = (
-                existing + json_mode_args if existing else json_mode_args
-            )
+            chat_completion_message["content"] = existing + json_mode_args if existing else json_mode_args
 
         real_tools = [t for i, t in enumerate(tools) if i not in json_tool_indices]
         return real_tools if real_tools else None
@@ -1980,9 +1822,7 @@ def _transform_response(  # noqa: PLR0915
         chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"}
         content_str = ""
         tools: List[ChatCompletionToolCallChunk] = []
-        reasoningContentBlocks: Optional[
-            List[BedrockConverseReasoningContentBlock]
-        ] = None
+        reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = None
         citationsContentBlocks: Optional[List[CitationsContentBlock]] = None
 
         if message is not None:
@@ -2001,17 +1841,11 @@ def _transform_response(  # noqa: PLR0915
             provider_specific_fields["citationsContent"] = citationsContentBlocks
 
         if provider_specific_fields:
-            chat_completion_message[
-                "provider_specific_fields"
-            ] = provider_specific_fields
+            chat_completion_message["provider_specific_fields"] = provider_specific_fields
 
         if reasoningContentBlocks is not None:
-            chat_completion_message[
-                "reasoning_content"
-            ] = self._transform_reasoning_content(reasoningContentBlocks)
-            chat_completion_message[
-                "thinking_blocks"
-            ] = self._transform_thinking_blocks(reasoningContentBlocks)
+            chat_completion_message["reasoning_content"] = self._transform_reasoning_content(reasoningContentBlocks)
+            chat_completion_message["thinking_blocks"] = self._transform_thinking_blocks(reasoningContentBlocks)
         chat_completion_message["content"] = content_str
         filtered_tools = self._filter_json_mode_tools(
             json_mode=json_mode,
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 879dd42be47..34261ba2391 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -722,7 +722,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "anthropic.claude-haiku-4-5@20251001": {
         "cache_creation_input_token_cost": 1.25e-06,
@@ -745,7 +746,8 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
-        "supports_native_streaming": true
+        "supports_native_streaming": true,
+        "supports_native_structured_output": true
     },
     "anthropic.claude-3-5-sonnet-20240620-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -967,7 +969,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
     },
     "anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.25e-06,
@@ -997,7 +1000,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "global.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.25e-06,
@@ -1027,7 +1031,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "us.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.875e-06,
@@ -1057,7 +1062,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "eu.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.875e-06,
@@ -1087,7 +1093,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "au.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.875e-06,
@@ -1117,7 +1124,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -1147,7 +1155,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "global.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -1177,7 +1186,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "us.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 4.125e-06,
@@ -1207,7 +1217,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "eu.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 4.125e-06,
@@ -1237,7 +1248,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "au.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 4.125e-06,
@@ -1267,7 +1279,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "anthropic.claude-sonnet-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -1327,7 +1340,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
     },
     "anthropic.claude-v1": {
         "input_cost_per_token": 8e-06,
@@ -1577,7 +1591,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "apac.anthropic.claude-3-sonnet-20240229-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -1665,7 +1680,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "azure/ada": {
         "input_cost_per_token": 1e-07,
@@ -11737,7 +11753,8 @@
         "output_cost_per_token": 1.68e-06,
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
     },
     "deepseek.v3.2": {
         "input_cost_per_token": 6.2e-07,
@@ -12182,7 +12199,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "eu.anthropic.claude-3-5-sonnet-20240620-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -12396,7 +12414,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "eu.meta.llama3-2-1b-instruct-v1:0": {
         "input_cost_per_token": 1.3e-07,
@@ -14626,18 +14645,6 @@
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
         "uses_embed_content": true
     },
-    "vertex_ai/gemini-embedding-2-preview": {
-        "input_cost_per_token": 1.5e-07,
-        "litellm_provider": "vertex_ai",
-        "max_input_tokens": 8192,
-        "max_tokens": 8192,
-        "mode": "embedding",
-        "output_cost_per_token": 0,
-        "output_vector_size": 3072,
-        "source": "https://ai.google.dev/gemini-api/docs/embeddings#multimodal",
-        "supports_multimodal": true,
-        "uses_embed_content": true
-    },
     "gemini/gemini-embedding-001": {
         "input_cost_per_token": 1.5e-07,
         "litellm_provider": "gemini",
@@ -16765,7 +16772,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "global.anthropic.claude-sonnet-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -16817,7 +16825,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "global.amazon.nova-2-lite-v1:0": {
         "cache_read_input_token_cost": 7.5e-08,
@@ -20420,7 +20429,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "jp.anthropic.claude-haiku-4-5-20251001-v1:0": {
         "cache_creation_input_token_cost": 1.375e-06,
@@ -20442,7 +20452,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "lambda_ai/deepseek-llama3.3-70b": {
         "input_cost_per_token": 2e-07,
@@ -21137,7 +21148,8 @@
         "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 1.2e-06,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "minimax.minimax-m2.1": {
         "input_cost_per_token": 3e-07,
@@ -21293,7 +21305,8 @@
         "mode": "chat",
         "output_cost_per_token": 2e-07,
         "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "mistral.ministral-3-3b-instruct": {
         "input_cost_per_token": 1e-07,
@@ -21304,7 +21317,8 @@
         "mode": "chat",
         "output_cost_per_token": 1e-07,
         "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "mistral.ministral-3-8b-instruct": {
         "input_cost_per_token": 1.5e-07,
@@ -21315,7 +21329,8 @@
         "mode": "chat",
         "output_cost_per_token": 1.5e-07,
         "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "mistral.mistral-7b-instruct-v0:2": {
         "input_cost_per_token": 1.5e-07,
@@ -21357,7 +21372,8 @@
         "mode": "chat",
         "output_cost_per_token": 1.5e-06,
         "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "mistral.mistral-small-2402-v1:0": {
         "input_cost_per_token": 1e-06,
@@ -21388,7 +21404,8 @@
         "mode": "chat",
         "output_cost_per_token": 4e-08,
         "supports_audio_input": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "mistral.voxtral-small-24b-2507": {
         "input_cost_per_token": 1e-07,
@@ -21399,7 +21416,8 @@
         "mode": "chat",
         "output_cost_per_token": 3e-07,
         "supports_audio_input": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "mistral/codestral-2405": {
         "input_cost_per_token": 1e-06,
@@ -22086,7 +22104,8 @@
         "mode": "chat",
         "output_cost_per_token": 2.5e-06,
         "supports_reasoning": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "moonshotai.kimi-k2.5": {
         "input_cost_per_token": 6e-07,
@@ -22961,7 +22980,8 @@
         "supports_function_calling": true,
         "supports_system_messages": true,
         "supports_tool_choice": true,
-        "source": "https://aws.amazon.com/bedrock/pricing/"
+        "source": "https://aws.amazon.com/bedrock/pricing/",
+        "supports_native_structured_output": true
     },
     "o1": {
         "cache_read_input_token_cost": 7.5e-06,
@@ -26045,7 +26065,8 @@
         "output_cost_per_token": 1.8e-06,
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
     },
     "qwen.qwen3-235b-a22b-2507-v1:0": {
         "input_cost_per_token": 2.2e-07,
@@ -26057,7 +26078,8 @@
         "output_cost_per_token": 8.8e-07,
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
     },
     "qwen.qwen3-coder-30b-a3b-v1:0": {
         "input_cost_per_token": 1.5e-07,
@@ -26069,7 +26091,8 @@
         "output_cost_per_token": 6e-07,
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
     },
     "qwen.qwen3-32b-v1:0": {
         "input_cost_per_token": 1.5e-07,
@@ -26081,7 +26104,8 @@
         "output_cost_per_token": 6e-07,
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
     },
     "qwen.qwen3-next-80b-a3b": {
         "input_cost_per_token": 1.5e-07,
@@ -26092,7 +26116,8 @@
         "mode": "chat",
         "output_cost_per_token": 1.2e-06,
         "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "qwen.qwen3-vl-235b-a22b": {
         "input_cost_per_token": 5.3e-07,
@@ -26104,7 +26129,8 @@
         "output_cost_per_token": 2.66e-06,
         "supports_function_calling": true,
         "supports_system_messages": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "supports_native_structured_output": true
     },
     "qwen.qwen3-coder-next": {
         "input_cost_per_token": 5e-07,
@@ -28129,7 +28155,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "us.anthropic.claude-3-5-sonnet-20240620-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -28287,7 +28314,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "au.anthropic.claude-haiku-4-5-20251001-v1:0": {
         "cache_creation_input_token_cost": 1.375e-06,
@@ -28308,7 +28336,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "us.anthropic.claude-opus-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 1.875e-05,
@@ -28360,7 +28389,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
     },
     "global.anthropic.claude-opus-4-5-20251101-v1:0": {
         "cache_creation_input_token_cost": 6.25e-06,
@@ -28386,7 +28416,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
     },
     "eu.anthropic.claude-opus-4-5-20251101-v1:0": {
         "cache_creation_input_token_cost": 6.25e-06,
@@ -28412,7 +28443,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
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     "us.anthropic.claude-sonnet-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -31013,7 +31045,9 @@
         "mode": "chat",
         "output_cost_per_token": 3.2e-06,
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#glm-models",
-        "supported_regions": ["global"],
+        "supported_regions": [
+            "global"
+        ],
         "supports_function_calling": true,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 879dd42be47..34261ba2391 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -722,7 +722,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "anthropic.claude-haiku-4-5@20251001": {
         "cache_creation_input_token_cost": 1.25e-06,
@@ -745,7 +746,8 @@
         "supports_tool_choice": true,
         "supports_vision": true,
         "tool_use_system_prompt_tokens": 346,
-        "supports_native_streaming": true
+        "supports_native_streaming": true,
+        "supports_native_structured_output": true
     },
     "anthropic.claude-3-5-sonnet-20240620-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -967,7 +969,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
     },
     "anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.25e-06,
@@ -997,7 +1000,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "global.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.25e-06,
@@ -1027,7 +1031,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "us.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.875e-06,
@@ -1057,7 +1062,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "eu.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.875e-06,
@@ -1087,7 +1093,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "au.anthropic.claude-opus-4-6-v1": {
         "cache_creation_input_token_cost": 6.875e-06,
@@ -1117,7 +1124,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -1147,7 +1155,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "global.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -1177,7 +1186,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "us.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 4.125e-06,
@@ -1207,7 +1217,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "eu.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 4.125e-06,
@@ -1237,7 +1248,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "au.anthropic.claude-sonnet-4-6": {
         "cache_creation_input_token_cost": 4.125e-06,
@@ -1267,7 +1279,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "anthropic.claude-sonnet-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -1327,7 +1340,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
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     "anthropic.claude-v1": {
         "input_cost_per_token": 8e-06,
@@ -1577,7 +1591,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "apac.anthropic.claude-3-sonnet-20240229-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -1665,7 +1680,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "azure/ada": {
         "input_cost_per_token": 1e-07,
@@ -11737,7 +11753,8 @@
         "output_cost_per_token": 1.68e-06,
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
     },
     "deepseek.v3.2": {
         "input_cost_per_token": 6.2e-07,
@@ -12182,7 +12199,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "eu.anthropic.claude-3-5-sonnet-20240620-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -12396,7 +12414,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "eu.meta.llama3-2-1b-instruct-v1:0": {
         "input_cost_per_token": 1.3e-07,
@@ -14626,18 +14645,6 @@
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
         "uses_embed_content": true
     },
-    "vertex_ai/gemini-embedding-2-preview": {
-        "input_cost_per_token": 1.5e-07,
-        "litellm_provider": "vertex_ai",
-        "max_input_tokens": 8192,
-        "max_tokens": 8192,
-        "mode": "embedding",
-        "output_cost_per_token": 0,
-        "output_vector_size": 3072,
-        "source": "https://ai.google.dev/gemini-api/docs/embeddings#multimodal",
-        "supports_multimodal": true,
-        "uses_embed_content": true
-    },
     "gemini/gemini-embedding-001": {
         "input_cost_per_token": 1.5e-07,
         "litellm_provider": "gemini",
@@ -16765,7 +16772,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "global.anthropic.claude-sonnet-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -16817,7 +16825,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "global.amazon.nova-2-lite-v1:0": {
         "cache_read_input_token_cost": 7.5e-08,
@@ -20420,7 +20429,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "jp.anthropic.claude-haiku-4-5-20251001-v1:0": {
         "cache_creation_input_token_cost": 1.375e-06,
@@ -20442,7 +20452,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
     },
     "lambda_ai/deepseek-llama3.3-70b": {
         "input_cost_per_token": 2e-07,
@@ -21137,7 +21148,8 @@
         "max_tokens": 8192,
         "mode": "chat",
         "output_cost_per_token": 1.2e-06,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "minimax.minimax-m2.1": {
         "input_cost_per_token": 3e-07,
@@ -21293,7 +21305,8 @@
         "mode": "chat",
         "output_cost_per_token": 2e-07,
         "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "mistral.ministral-3-3b-instruct": {
         "input_cost_per_token": 1e-07,
@@ -21304,7 +21317,8 @@
         "mode": "chat",
         "output_cost_per_token": 1e-07,
         "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "mistral.ministral-3-8b-instruct": {
         "input_cost_per_token": 1.5e-07,
@@ -21315,7 +21329,8 @@
         "mode": "chat",
         "output_cost_per_token": 1.5e-07,
         "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "mistral.mistral-7b-instruct-v0:2": {
         "input_cost_per_token": 1.5e-07,
@@ -21357,7 +21372,8 @@
         "mode": "chat",
         "output_cost_per_token": 1.5e-06,
         "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "mistral.mistral-small-2402-v1:0": {
         "input_cost_per_token": 1e-06,
@@ -21388,7 +21404,8 @@
         "mode": "chat",
         "output_cost_per_token": 4e-08,
         "supports_audio_input": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "mistral.voxtral-small-24b-2507": {
         "input_cost_per_token": 1e-07,
@@ -21399,7 +21416,8 @@
         "mode": "chat",
         "output_cost_per_token": 3e-07,
         "supports_audio_input": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "mistral/codestral-2405": {
         "input_cost_per_token": 1e-06,
@@ -22086,7 +22104,8 @@
         "mode": "chat",
         "output_cost_per_token": 2.5e-06,
         "supports_reasoning": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
     },
     "moonshotai.kimi-k2.5": {
         "input_cost_per_token": 6e-07,
@@ -22961,7 +22980,8 @@
         "supports_function_calling": true,
         "supports_system_messages": true,
         "supports_tool_choice": true,
-        "source": "https://aws.amazon.com/bedrock/pricing/"
+        "source": "https://aws.amazon.com/bedrock/pricing/",
+        "supports_native_structured_output": true
     },
     "o1": {
         "cache_read_input_token_cost": 7.5e-06,
@@ -26045,7 +26065,8 @@
         "output_cost_per_token": 1.8e-06,
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
     },
     "qwen.qwen3-235b-a22b-2507-v1:0": {
         "input_cost_per_token": 2.2e-07,
@@ -26057,7 +26078,8 @@
         "output_cost_per_token": 8.8e-07,
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
     },
     "qwen.qwen3-coder-30b-a3b-v1:0": {
         "input_cost_per_token": 1.5e-07,
@@ -26069,7 +26091,8 @@
         "output_cost_per_token": 6e-07,
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
     },
     "qwen.qwen3-32b-v1:0": {
         "input_cost_per_token": 1.5e-07,
@@ -26081,7 +26104,8 @@
         "output_cost_per_token": 6e-07,
         "supports_function_calling": true,
         "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
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     "qwen.qwen3-next-80b-a3b": {
         "input_cost_per_token": 1.5e-07,
@@ -26092,7 +26116,8 @@
         "mode": "chat",
         "output_cost_per_token": 1.2e-06,
         "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
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     "qwen.qwen3-vl-235b-a22b": {
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@@ -26104,7 +26129,8 @@
         "output_cost_per_token": 2.66e-06,
         "supports_function_calling": true,
         "supports_system_messages": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "supports_native_structured_output": true
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     "qwen.qwen3-coder-next": {
         "input_cost_per_token": 5e-07,
@@ -28129,7 +28155,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "us.anthropic.claude-3-5-sonnet-20240620-v1:0": {
         "input_cost_per_token": 3e-06,
@@ -28287,7 +28314,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "au.anthropic.claude-haiku-4-5-20251001-v1:0": {
         "cache_creation_input_token_cost": 1.375e-06,
@@ -28308,7 +28336,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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     "us.anthropic.claude-opus-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 1.875e-05,
@@ -28360,7 +28389,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
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     "global.anthropic.claude-opus-4-5-20251101-v1:0": {
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@@ -28386,7 +28416,8 @@
         "supports_response_schema": true,
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-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
     },
     "eu.anthropic.claude-opus-4-5-20251101-v1:0": {
         "cache_creation_input_token_cost": 6.25e-06,
@@ -28412,7 +28443,8 @@
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
     },
     "us.anthropic.claude-sonnet-4-20250514-v1:0": {
         "cache_creation_input_token_cost": 3.75e-06,
@@ -31013,7 +31045,9 @@
         "mode": "chat",
         "output_cost_per_token": 3.2e-06,
         "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#glm-models",
-        "supported_regions": ["global"],
+        "supported_regions": [
+            "global"
+        ],
         "supports_function_calling": true,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
diff --git a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
index a305009659c..65825419d43 100644
--- a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
+++ b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
@@ -6,9 +6,7 @@
 import pytest
 from fastapi.testclient import TestClient
 
-sys.path.insert(
-    0, os.path.abspath("../../../../..")
-)  # Adds the parent directory to the system path
+sys.path.insert(0, os.path.abspath("../../../../.."))  # Adds the parent directory to the system path
 from unittest.mock import MagicMock, patch
 
 import litellm
@@ -37,10 +35,7 @@ def test_transform_usage():
     )
     assert openai_usage.completion_tokens == usage["outputTokens"]
     assert openai_usage.total_tokens == usage["totalTokens"]
-    assert (
-        openai_usage.prompt_tokens_details.cached_tokens
-        == usage["cacheReadInputTokens"]
-    )
+    assert openai_usage.prompt_tokens_details.cached_tokens == usage["cacheReadInputTokens"]
     assert openai_usage._cache_creation_input_tokens == usage["cacheWriteInputTokens"]
     assert openai_usage._cache_read_input_tokens == usage["cacheReadInputTokens"]
     # completion_tokens_details should always be populated
@@ -194,14 +189,10 @@ def test_apply_tool_call_transformation_if_needed():
         role="user",
         content=json.dumps(tool_response),
     )
-    transformed_message, _ = config.apply_tool_call_transformation_if_needed(
-        message, tool_calls
-    )
+    transformed_message, _ = config.apply_tool_call_transformation_if_needed(message, tool_calls)
     assert len(transformed_message.tool_calls) == 1
     assert transformed_message.tool_calls[0].function.name == "test_function"
-    assert transformed_message.tool_calls[0].function.arguments == json.dumps(
-        tool_response["parameters"]
-    )
+    assert transformed_message.tool_calls[0].function.arguments == json.dumps(tool_response["parameters"])
 
 
 def test_transform_tool_call_with_cache_control():
@@ -250,12 +241,7 @@ def test_transform_tool_call_with_cache_control():
     print(function_out_msg)
     assert function_out_msg["toolSpec"]["name"] == "get_location"
     assert function_out_msg["toolSpec"]["description"] == "Get the user's location"
-    assert (
-        function_out_msg["toolSpec"]["inputSchema"]["json"]["properties"]["location"][
-            "type"
-        ]
-        == "string"
-    )
+    assert function_out_msg["toolSpec"]["inputSchema"]["json"]["properties"]["location"]["type"] == "string"
 
     transformed_cache_msg = result["toolConfig"]["tools"][1]
     assert "cachePoint" in transformed_cache_msg
@@ -285,6 +271,7 @@ def test_reasoning_with_forced_tool_choice_switches_to_auto():
 
     assert optional_params["tool_choice"] == {"auto": {}}
 
+
 def test_get_supported_openai_params():
     config = AmazonConverseConfig()
     supported_params = config.get_supported_openai_params(
@@ -307,15 +294,13 @@ def test_get_supported_openai_params_bedrock_converse():
     for model in litellm.BEDROCK_CONVERSE_MODELS:
         print(f"Testing model: {model}")
         config = AmazonConverseConfig()
-        supported_params_without_prefix = config.get_supported_openai_params(
-            model=model
-        )
+        supported_params_without_prefix = config.get_supported_openai_params(model=model)
 
-        supported_params_with_prefix = config.get_supported_openai_params(
-            model=f"bedrock/converse/{model}"
-        )
+        supported_params_with_prefix = config.get_supported_openai_params(model=f"bedrock/converse/{model}")
 
-        assert set(supported_params_without_prefix) == set(supported_params_with_prefix), f"Supported params mismatch for model: {model}. Without prefix: {supported_params_without_prefix}, With prefix: {supported_params_with_prefix}"
+        assert set(supported_params_without_prefix) == set(supported_params_with_prefix), (
+            f"Supported params mismatch for model: {model}. Without prefix: {supported_params_without_prefix}, With prefix: {supported_params_with_prefix}"
+        )
         print(f"✅ Passed for model: {model}")
 
 
@@ -382,7 +367,7 @@ def test_transform_response_with_computer_use_tool():
                             },
                         }
                     }
-                ]
+                ],
             }
         },
         "stopReason": "tool_use",
@@ -396,10 +381,12 @@ def test_transform_response_with_computer_use_tool():
             "cacheWriteInputTokens": 0,
         },
     }
+
     # Mock httpx.Response
     class MockResponse:
         def json(self):
             return response_json
+
         @property
         def text(self):
             return json.dumps(response_json)
@@ -468,12 +455,10 @@ def test_transform_response_with_bash_tool():
                         "toolUse": {
                             "toolUseId": "tooluse_456",
                             "name": "bash",
-                            "input": {
-                                "command": "ls -la *.py"
-                            },
+                            "input": {"command": "ls -la *.py"},
                         }
                     }
-                ]
+                ],
             }
         },
         "stopReason": "tool_use",
@@ -487,10 +472,12 @@ def test_transform_response_with_bash_tool():
             "cacheWriteInputTokens": 0,
         },
     }
+
     # Mock httpx.Response
     class MockResponse:
         def json(self):
             return response_json
+
         @property
         def text(self):
             return json.dumps(response_json)
@@ -549,10 +536,11 @@ def test_transform_response_with_structured_response_being_called():
                             "name": "json_tool_call",
                             "input": {
                                 "Current_Temperature": 62,
-                                "Weather_Explanation": "San Francisco typically has mild, cool weather year-round due to its coastal location and marine influence. The city is known for its fog, moderate temperatures, and relatively stable climate with little seasonal variation."},
+                                "Weather_Explanation": "San Francisco typically has mild, cool weather year-round due to its coastal location and marine influence. The city is known for its fog, moderate temperatures, and relatively stable climate with little seasonal variation.",
+                            },
                         }
                     }
-                ]
+                ],
             }
         },
         "stopReason": "tool_use",
@@ -566,10 +554,12 @@ def test_transform_response_with_structured_response_being_called():
             "cacheWriteInputTokens": 0,
         },
     }
+
     # Mock httpx.Response
     class MockResponse:
         def json(self):
             return response_json
+
         @property
         def text(self):
             return json.dumps(response_json)
@@ -580,49 +570,43 @@ def text(self):
         "json_mode": True,
         "tools": [
             {
-                'type': 'function',
-                'function': {
-                    'name': 'get_weather',
-                    'description': 'Get the current weather in a given location',
-                    'parameters': {
-                        'type': 'object',
-                        'properties': {
-                            'location': {
-                                'type': 'string',
-                                'description': 'The city and state, e.g. San Francisco, CA'
-                            },
-                            'unit': {
-                                'type': 'string',
-                                'enum': ['celsius', 'fahrenheit']
-                            }
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "Get the current weather in a given location",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"},
+                            "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                         },
-                        'required': ['location']
-                    }
-                }
+                        "required": ["location"],
+                    },
+                },
             },
             {
-                'type': 'function',
-                'function': {
-                    'name': 'json_tool_call',
-                    'parameters': {
-                        '$schema': 'http://json-schema.org/draft-07/schema#',
-                        'type': 'object',
-                        'required': ['Weather_Explanation', 'Current_Temperature'],
-                        'properties': {
-                            'Weather_Explanation': {
-                                'type': ['string', 'null'],
-                                'description': '1-2 sentences explaining the weather in the location'
+                "type": "function",
+                "function": {
+                    "name": "json_tool_call",
+                    "parameters": {
+                        "$schema": "http://json-schema.org/draft-07/schema#",
+                        "type": "object",
+                        "required": ["Weather_Explanation", "Current_Temperature"],
+                        "properties": {
+                            "Weather_Explanation": {
+                                "type": ["string", "null"],
+                                "description": "1-2 sentences explaining the weather in the location",
+                            },
+                            "Current_Temperature": {
+                                "type": ["number", "null"],
+                                "description": "Current temperature in the location",
                             },
-                            'Current_Temperature': {
-                                'type': ['number', 'null'],
-                                'description': 'Current temperature in the location'
-                            }
                         },
-                        'additionalProperties': False
-                    }
-                }
-            }
-        ]
+                        "additionalProperties": False,
+                    },
+                },
+            },
+        ],
     }
     # Call the transformation logic
     result = config._transform_response(
@@ -641,7 +625,11 @@ def text(self):
     assert result.choices[0].message.tool_calls is None
 
     assert result.choices[0].message.content is not None
-    assert result.choices[0].message.content ==  '{"Current_Temperature": 62, "Weather_Explanation": "San Francisco typically has mild, cool weather year-round due to its coastal location and marine influence. The city is known for its fog, moderate temperatures, and relatively stable climate with little seasonal variation."}'
+    assert (
+        result.choices[0].message.content
+        == '{"Current_Temperature": 62, "Weather_Explanation": "San Francisco typically has mild, cool weather year-round due to its coastal location and marine influence. The city is known for its fog, moderate temperatures, and relatively stable climate with little seasonal variation."}'
+    )
+
 
 def test_transform_response_with_structured_response_calling_tool():
     """Test response transformation with structured response."""
@@ -650,28 +638,20 @@ def test_transform_response_with_structured_response_calling_tool():
 
     # Simulate a Bedrock Converse response with a bash tool call
     response_json = {
-        "metrics": {
-            "latencyMs": 1148
-        },
+        "metrics": {"latencyMs": 1148},
         "output": {
-            "message":
-            {
+            "message": {
                 "content": [
-                    {
-                        "text": "I\'ll check the current weather in San Francisco for you."
-                    },
+                    {"text": "I'll check the current weather in San Francisco for you."},
                     {
                         "toolUse": {
-                            "input": {
-                                "location": "San Francisco, CA",
-                                "unit": "celsius"
-                            },
+                            "input": {"location": "San Francisco, CA", "unit": "celsius"},
                             "name": "get_weather",
-                            "toolUseId": "tooluse_oKk__QrqSUmufMw3Q7vGaQ"
+                            "toolUseId": "tooluse_oKk__QrqSUmufMw3Q7vGaQ",
                         }
-                    }
+                    },
                 ],
-                "role": "assistant"
+                "role": "assistant",
             }
         },
         "stopReason": "tool_use",
@@ -682,13 +662,15 @@ def test_transform_response_with_structured_response_calling_tool():
             "cacheWriteInputTokens": 0,
             "inputTokens": 534,
             "outputTokens": 69,
-            "totalTokens": 603
-        }
+            "totalTokens": 603,
+        },
     }
+
     # Mock httpx.Response
     class MockResponse:
         def json(self):
             return response_json
+
         @property
         def text(self):
             return json.dumps(response_json)
@@ -699,49 +681,43 @@ def text(self):
         "json_mode": True,
         "tools": [
             {
-                'type': 'function',
-                'function': {
-                    'name': 'get_weather',
-                    'description': 'Get the current weather in a given location',
-                    'parameters': {
-                        'type': 'object',
-                        'properties': {
-                            'location': {
-                                'type': 'string',
-                                'description': 'The city and state, e.g. San Francisco, CA'
-                            },
-                            'unit': {
-                                'type': 'string',
-                                'enum': ['celsius', 'fahrenheit']
-                            }
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "Get the current weather in a given location",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"},
+                            "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                         },
-                        'required': ['location']
-                    }
-                }
+                        "required": ["location"],
+                    },
+                },
             },
             {
-                'type': 'function',
-                'function': {
-                    'name': 'json_tool_call',
-                    'parameters': {
-                        '$schema': 'http://json-schema.org/draft-07/schema#',
-                        'type': 'object',
-                        'required': ['Weather_Explanation', 'Current_Temperature'],
-                        'properties': {
-                            'Weather_Explanation': {
-                                'type': ['string', 'null'],
-                                'description': '1-2 sentences explaining the weather in the location'
+                "type": "function",
+                "function": {
+                    "name": "json_tool_call",
+                    "parameters": {
+                        "$schema": "http://json-schema.org/draft-07/schema#",
+                        "type": "object",
+                        "required": ["Weather_Explanation", "Current_Temperature"],
+                        "properties": {
+                            "Weather_Explanation": {
+                                "type": ["string", "null"],
+                                "description": "1-2 sentences explaining the weather in the location",
+                            },
+                            "Current_Temperature": {
+                                "type": ["number", "null"],
+                                "description": "Current temperature in the location",
                             },
-                            'Current_Temperature': {
-                                'type': ['number', 'null'],
-                                'description': 'Current temperature in the location'
-                            }
                         },
-                        'additionalProperties': False
-                    }
-                }
-            }
-        ]
+                        "additionalProperties": False,
+                    },
+                },
+            },
+        ],
     }
     # Call the transformation logic
     result = config._transform_response(
@@ -760,7 +736,10 @@ def text(self):
     assert result.choices[0].message.tool_calls is not None
     assert len(result.choices[0].message.tool_calls) == 1
     assert result.choices[0].message.tool_calls[0].function.name == "get_weather"
-    assert result.choices[0].message.tool_calls[0].function.arguments == '{"location": "San Francisco, CA", "unit": "celsius"}'
+    assert (
+        result.choices[0].message.tool_calls[0].function.arguments
+        == '{"location": "San Francisco, CA", "unit": "celsius"}'
+    )
 
 
 @pytest.mark.asyncio
@@ -775,12 +754,7 @@ async def test_bedrock_bash_tool_acompletion():
         }
     ]
 
-    messages = [
-        {
-            "role": "user",
-            "content": "run ls command and find all python files"
-        }
-    ]
+    messages = [{"role": "user", "content": "run ls command and find all python files"}]
 
     try:
         response = await litellm.acompletion(
@@ -788,7 +762,7 @@ async def test_bedrock_bash_tool_acompletion():
             messages=messages,
             tools=tools,
             # Using dummy API key - test should fail with auth error, proving request formatting works
-            api_key="dummy-key-for-testing"
+            api_key="dummy-key-for-testing",
         )
         # If we get here, something's wrong - we expect an auth error
         assert False, "Expected authentication error but got successful response"
@@ -797,8 +771,16 @@ async def test_bedrock_bash_tool_acompletion():
 
         # Check if it's an expected authentication/credentials error
         auth_error_indicators = [
-            "credentials", "authentication", "unauthorized", "access denied",
-            "aws", "region", "profile", "token", "invalid", "signature"
+            "credentials",
+            "authentication",
+            "unauthorized",
+            "access denied",
+            "aws",
+            "region",
+            "profile",
+            "token",
+            "invalid",
+            "signature",
         ]
 
         if any(auth_error in error_str for auth_error in auth_error_indicators):
@@ -828,17 +810,14 @@ async def test_bedrock_computer_use_acompletion():
         {
             "role": "user",
             "content": [
-                {
-                    "type": "text",
-                    "text": "Go to the bedrock console"
-                },
+                {"type": "text", "text": "Go to the bedrock console"},
                 {
                     "type": "image_url",
                     "image_url": {
                         "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg=="
-                    }
-                }
-            ]
+                    },
+                },
+            ],
         }
     ]
 
@@ -848,7 +827,7 @@ async def test_bedrock_computer_use_acompletion():
             messages=messages,
             tools=tools,
             # Using dummy API key - test should fail with auth error, proving request formatting works
-            api_key="dummy-key-for-testing"
+            api_key="dummy-key-for-testing",
         )
         # If we get here, something's wrong - we expect an auth error
         assert False, "Expected authentication error but got successful response"
@@ -857,8 +836,16 @@ async def test_bedrock_computer_use_acompletion():
 
         # Check if it's an expected authentication/credentials error
         auth_error_indicators = [
-            "credentials", "authentication", "unauthorized", "access denied",
-            "aws", "region", "profile", "token", "invalid", "signature"
+            "credentials",
+            "authentication",
+            "unauthorized",
+            "access denied",
+            "aws",
+            "region",
+            "profile",
+            "token",
+            "invalid",
+            "signature",
         ]
 
         if any(auth_error in error_str for auth_error in auth_error_indicators):
@@ -886,15 +873,10 @@ async def test_transformation_directly():
         {
             "type": "bash_20241022",
             "name": "bash",
-        }
+        },
     ]
 
-    messages = [
-        {
-            "role": "user",
-            "content": "run ls command and find all python files"
-        }
-    ]
+    messages = [{"role": "user", "content": "run ls command and find all python files"}]
 
     # Transform request
     request_data = config.transform_request(
@@ -902,7 +884,7 @@ async def test_transformation_directly():
         messages=messages,
         optional_params={"tools": tools},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # Verify the structure
@@ -994,16 +976,11 @@ def test_transform_request_with_multiple_tools():
                     },
                     "required": ["location"],
                 },
-            }
-        }
+            },
+        },
     ]
 
-    messages = [
-        {
-            "role": "user",
-            "content": "run ls command and find all python files"
-        }
-    ]
+    messages = [{"role": "user", "content": "run ls command and find all python files"}]
 
     # Transform request
     request_data = config.transform_request(
@@ -1011,7 +988,7 @@ def test_transform_request_with_multiple_tools():
         messages=messages,
         optional_params={"tools": tools},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # Verify the structure
@@ -1054,17 +1031,14 @@ def test_transform_request_with_computer_tool_only():
         {
             "role": "user",
             "content": [
-                {
-                    "type": "text",
-                    "text": "Go to the bedrock console"
-                },
+                {"type": "text", "text": "Go to the bedrock console"},
                 {
                     "type": "image_url",
                     "image_url": {
                         "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg=="
-                    }
-                }
-            ]
+                    },
+                },
+            ],
         }
     ]
 
@@ -1074,7 +1048,7 @@ def test_transform_request_with_computer_tool_only():
         messages=messages,
         optional_params={"tools": tools},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # Verify the structure
@@ -1102,12 +1076,7 @@ def test_transform_request_with_bash_tool_only():
         }
     ]
 
-    messages = [
-        {
-            "role": "user",
-            "content": "run ls command and find all python files"
-        }
-    ]
+    messages = [{"role": "user", "content": "run ls command and find all python files"}]
 
     # Transform request
     request_data = config.transform_request(
@@ -1115,7 +1084,7 @@ def test_transform_request_with_bash_tool_only():
         messages=messages,
         optional_params={"tools": tools},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # Verify the structure
@@ -1143,12 +1112,7 @@ def test_transform_request_with_text_editor_tool():
         }
     ]
 
-    messages = [
-        {
-            "role": "user",
-            "content": "Edit this text file"
-        }
-    ]
+    messages = [{"role": "user", "content": "Edit this text file"}]
 
     # Transform request
     request_data = config.transform_request(
@@ -1156,7 +1120,7 @@ def test_transform_request_with_text_editor_tool():
         messages=messages,
         optional_params={"tools": tools},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # Verify the structure
@@ -1194,16 +1158,11 @@ def test_transform_request_with_function_tool():
                     },
                     "required": ["location"],
                 },
-            }
+            },
         }
     ]
 
-    messages = [
-        {
-            "role": "user",
-            "content": "What's the weather like in San Francisco?"
-        }
-    ]
+    messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}]
 
     # Transform request
     request_data = config.transform_request(
@@ -1211,7 +1170,7 @@ def test_transform_request_with_function_tool():
         messages=messages,
         optional_params={"tools": tools},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # Verify the structure
@@ -1247,7 +1206,7 @@ def test_map_openai_params_with_response_format():
                     },
                     "required": ["location"],
                 },
-            }
+            },
         }
     ]
 
@@ -1279,7 +1238,7 @@ def test_map_openai_params_with_response_format():
         non_default_params={"response_format": json_schema},
         optional_params={"tools": tools},
         model="eu.anthropic.claude-sonnet-4-20250514-v1:0",
-        drop_params=False
+        drop_params=False,
     )
 
     assert "tools" in optional_params
@@ -1299,31 +1258,21 @@ async def test_assistant_message_cache_control():
     # Test assistant message with string content and cache_control
     messages = [
         {"role": "user", "content": "Hello"},
-        {
-            "role": "assistant",
-            "content": "Hi there!",
-            "cache_control": {"type": "ephemeral"}
-        }
+        {"role": "assistant", "content": "Hi there!", "cache_control": {"type": "ephemeral"}},
     ]
 
     result = _bedrock_converse_messages_pt(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     assert result == async_result
 
     async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     assert result == async_result
@@ -1353,26 +1302,16 @@ async def test_assistant_message_list_content_cache_control():
         {"role": "user", "content": "Hello"},
         {
             "role": "assistant",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "This should be cached",
-                    "cache_control": {"type": "ephemeral"}
-                }
-            ]
-        }
+            "content": [{"type": "text", "text": "This should be cached", "cache_control": {"type": "ephemeral"}}],
+        },
     ]
 
     result = _bedrock_converse_messages_pt(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     assert result == async_result
@@ -1399,36 +1338,22 @@ async def test_tool_message_cache_control():
             "role": "assistant",
             "content": None,
             "tool_calls": [
-                {
-                    "id": "call_123",
-                    "type": "function",
-                    "function": {"name": "get_weather", "arguments": "{}"}
-                }
-            ]
+                {"id": "call_123", "type": "function", "function": {"name": "get_weather", "arguments": "{}"}}
+            ],
         },
         {
             "role": "tool",
             "tool_call_id": "call_123",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "Weather data: sunny, 25°C",
-                    "cache_control": {"type": "ephemeral"}
-                }
-            ]
-        }
+            "content": [{"type": "text", "text": "Weather data: sunny, 25°C", "cache_control": {"type": "ephemeral"}}],
+        },
     ]
 
     result = _bedrock_converse_messages_pt(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     assert result == async_result
@@ -1463,31 +1388,23 @@ async def test_tool_message_string_content_cache_control():
             "role": "assistant",
             "content": None,
             "tool_calls": [
-                {
-                    "id": "call_123",
-                    "type": "function",
-                    "function": {"name": "get_weather", "arguments": "{}"}
-                }
-            ]
+                {"id": "call_123", "type": "function", "function": {"name": "get_weather", "arguments": "{}"}}
+            ],
         },
         {
             "role": "tool",
             "tool_call_id": "call_123",
             "content": "Weather: sunny, 25°C",
-            "cache_control": {"type": "ephemeral"}
-        }
+            "cache_control": {"type": "ephemeral"},
+        },
     ]
 
     result = _bedrock_converse_messages_pt(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     assert result == async_result
@@ -1523,22 +1440,18 @@ async def test_assistant_tool_calls_cache_control():
                     "id": "call_proxy_123",
                     "type": "function",
                     "function": {"name": "calc", "arguments": "{}"},
-                    "cache_control": {"type": "ephemeral"}
+                    "cache_control": {"type": "ephemeral"},
                 }
-            ]
-        }
+            ],
+        },
     ]
 
     result = _bedrock_converse_messages_pt(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     assert result == async_result
@@ -1575,28 +1488,24 @@ async def test_multiple_tool_calls_with_mixed_cache_control():
                     "id": "call_1",
                     "type": "function",
                     "function": {"name": "calc", "arguments": '{"expr": "2+2"}'},
-                    "cache_control": {"type": "ephemeral"}
+                    "cache_control": {"type": "ephemeral"},
                 },
                 {
                     "id": "call_2",
                     "type": "function",
-                    "function": {"name": "calc", "arguments": '{"expr": "3+3"}'}
+                    "function": {"name": "calc", "arguments": '{"expr": "3+3"}'},
                     # No cache_control
-                }
-            ]
-        }
+                },
+            ],
+        },
     ]
 
     result = _bedrock_converse_messages_pt(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     assert result == async_result
@@ -1632,20 +1541,16 @@ async def test_no_cache_control_no_cache_point():
         {
             "role": "tool",
             "tool_call_id": "call_123",
-            "content": "Tool result"  # No cache_control
-        }
+            "content": "Tool result",  # No cache_control
+        },
     ]
 
     result = _bedrock_converse_messages_pt(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     async_result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
     )
 
     assert result == async_result
@@ -1665,6 +1570,7 @@ async def test_no_cache_control_no_cache_point():
 # Guarded Text Feature Tests
 # ============================================================================
 
+
 def test_guarded_text_wraps_in_guardrail_converse_content():
     """Test that guarded_text content type gets wrapped in guardContent blocks."""
     from litellm.litellm_core_utils.prompt_templates.factory import (
@@ -1677,15 +1583,13 @@ def test_guarded_text_wraps_in_guardrail_converse_content():
             "content": [
                 {"type": "text", "text": "Regular text content"},
                 {"type": "guarded_text", "text": "This should be guarded"},
-                {"type": "text", "text": "More regular text"}
-            ]
+                {"type": "text", "text": "More regular text"},
+            ],
         }
     ]
 
     result = _bedrock_converse_messages_pt(
-        messages=messages,
-        model="us.amazon.nova-pro-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="us.amazon.nova-pro-v1:0", llm_provider="bedrock_converse"
     )
 
     # Should have 1 message
@@ -1705,6 +1609,7 @@ def test_guarded_text_wraps_in_guardrail_converse_content():
     assert "guardContent" in content[1]
     assert content[1]["guardContent"]["text"]["text"] == "This should be guarded"
 
+
 def test_guarded_text_with_system_messages():
     """Test guarded_text with system messages using the full transformation."""
     config = AmazonConverseConfig()
@@ -1715,24 +1620,22 @@ def test_guarded_text_with_system_messages():
             "role": "user",
             "content": [
                 {"type": "text", "text": "What is the main topic of this legal document?"},
-                {"type": "guarded_text", "text": "This is a set of very long instructions that you will follow. Here is a legal document that you will use to answer the user's question."}
-            ]
-        }
+                {
+                    "type": "guarded_text",
+                    "text": "This is a set of very long instructions that you will follow. Here is a legal document that you will use to answer the user's question.",
+                },
+            ],
+        },
     ]
 
-    optional_params = {
-        "guardrailConfig": {
-            "guardrailIdentifier": "gr-abc123",
-            "guardrailVersion": "DRAFT"
-        }
-    }
+    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "DRAFT"}}
 
     result = config._transform_request(
         model="us.amazon.nova-pro-v1:0",
         messages=messages,
         optional_params=optional_params,
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # Should have system content blocks
@@ -1755,7 +1658,10 @@ def test_guarded_text_with_system_messages():
     assert content[0]["text"] == "What is the main topic of this legal document?"
     # Second should be guardContent
     assert "guardContent" in content[1]
-    assert content[1]["guardContent"]["text"]["text"] == "This is a set of very long instructions that you will follow. Here is a legal document that you will use to answer the user's question."
+    assert (
+        content[1]["guardContent"]["text"]["text"]
+        == "This is a set of very long instructions that you will follow. Here is a legal document that you will use to answer the user's question."
+    )
 
 
 def test_guarded_text_with_mixed_content_types():
@@ -1770,15 +1676,13 @@ def test_guarded_text_with_mixed_content_types():
             "content": [
                 {"type": "text", "text": "Look at this image"},
                 {"type": "image_url", "image_url": {"url": "data:image/png;base64,test"}},
-                {"type": "guarded_text", "text": "This sensitive content should be guarded"}
-            ]
+                {"type": "guarded_text", "text": "This sensitive content should be guarded"},
+            ],
         }
     ]
 
     result = _bedrock_converse_messages_pt(
-        messages=messages,
-        model="us.amazon.nova-pro-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="us.amazon.nova-pro-v1:0", llm_provider="bedrock_converse"
     )
 
     # Should have 1 message
@@ -1800,6 +1704,7 @@ def test_guarded_text_with_mixed_content_types():
     assert "guardContent" in content[2]
     assert content[2]["guardContent"]["text"]["text"] == "This sensitive content should be guarded"
 
+
 @pytest.mark.asyncio
 async def test_async_guarded_text():
     """Test async version of guarded_text processing."""
@@ -1810,17 +1715,12 @@ async def test_async_guarded_text():
     messages = [
         {
             "role": "user",
-            "content": [
-                {"type": "text", "text": "Hello"},
-                {"type": "guarded_text", "text": "This should be guarded"}
-            ]
+            "content": [{"type": "text", "text": "Hello"}, {"type": "guarded_text", "text": "This should be guarded"}],
         }
     ]
 
     result = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-        messages=messages,
-        model="us.amazon.nova-pro-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="us.amazon.nova-pro-v1:0", llm_provider="bedrock_converse"
     )
 
     # Should have 1 message
@@ -1851,31 +1751,21 @@ def test_guarded_text_with_tool_calls():
             "role": "user",
             "content": [
                 {"type": "text", "text": "What's the weather?"},
-                {"type": "guarded_text", "text": "Please be careful with sensitive information"}
-            ]
+                {"type": "guarded_text", "text": "Please be careful with sensitive information"},
+            ],
         },
         {
             "role": "assistant",
             "content": None,
             "tool_calls": [
-                {
-                    "id": "call_123",
-                    "type": "function",
-                    "function": {"name": "get_weather", "arguments": "{}"}
-                }
-            ]
+                {"id": "call_123", "type": "function", "function": {"name": "get_weather", "arguments": "{}"}}
+            ],
         },
-        {
-            "role": "tool",
-            "tool_call_id": "call_123",
-            "content": "It's sunny and 25°C"
-        }
+        {"role": "tool", "tool_call_id": "call_123", "content": "It's sunny and 25°C"},
     ]
 
     result = _bedrock_converse_messages_pt(
-        messages=messages,
-        model="us.amazon.nova-pro-v1:0",
-        llm_provider="bedrock_converse"
+        messages=messages, model="us.amazon.nova-pro-v1:0", llm_provider="bedrock_converse"
     )
 
     # Should have 3 messages
@@ -1909,26 +1799,18 @@ def test_guarded_text_guardrail_config_preserved():
     messages = [
         {
             "role": "user",
-            "content": [
-                {"type": "text", "text": "Hello"},
-                {"type": "guarded_text", "text": "This should be guarded"}
-            ]
+            "content": [{"type": "text", "text": "Hello"}, {"type": "guarded_text", "text": "This should be guarded"}],
         }
     ]
 
-    optional_params = {
-        "guardrailConfig": {
-            "guardrailIdentifier": "gr-abc123",
-            "guardrailVersion": "DRAFT"
-        }
-    }
+    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "DRAFT"}}
 
     result = config._transform_request(
         model="us.amazon.nova-pro-v1:0",
         messages=messages,
         optional_params=optional_params,
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # GuardrailConfig should be present at top level
@@ -1946,23 +1828,10 @@ def test_auto_convert_last_user_message_to_guarded_text():
     config = AmazonConverseConfig()
 
     messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "What is the main topic of this legal document?"
-                }
-            ]
-        }
+        {"role": "user", "content": [{"type": "text", "text": "What is the main topic of this legal document?"}]}
     ]
 
-    optional_params = {
-        "guardrailConfig": {
-            "guardrailIdentifier": "gr-abc123",
-            "guardrailVersion": "1"
-        }
-    }
+    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
 
     # Test the helper method directly
     converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
@@ -1979,19 +1848,9 @@ def test_auto_convert_last_user_message_string_content():
     """Test that last user message with string content is automatically converted to guarded_text when guardrailConfig is present."""
     config = AmazonConverseConfig()
 
-    messages = [
-        {
-            "role": "user",
-            "content": "What is the main topic of this legal document?"
-        }
-    ]
+    messages = [{"role": "user", "content": "What is the main topic of this legal document?"}]
 
-    optional_params = {
-        "guardrailConfig": {
-            "guardrailIdentifier": "gr-abc123",
-            "guardrailVersion": "1"
-        }
-    }
+    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
 
     # Test the helper method directly
     converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
@@ -2009,15 +1868,7 @@ def test_no_conversion_when_no_guardrail_config():
     config = AmazonConverseConfig()
 
     messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "What is the main topic of this legal document?"
-                }
-            ]
-        }
+        {"role": "user", "content": [{"type": "text", "text": "What is the main topic of this legal document?"}]}
     ]
 
     optional_params = {}
@@ -2033,24 +1884,9 @@ def test_no_conversion_when_guarded_text_already_present():
     """Test that no conversion happens when guarded_text is already present in the last user message."""
     config = AmazonConverseConfig()
 
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "guarded_text",
-                    "text": "This is already guarded"
-                }
-            ]
-        }
-    ]
+    messages = [{"role": "user", "content": [{"type": "guarded_text", "text": "This is already guarded"}]}]
 
-    optional_params = {
-        "guardrailConfig": {
-            "guardrailIdentifier": "gr-abc123",
-            "guardrailVersion": "1"
-        }
-    }
+    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
 
     # Test the helper method directly
     converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
@@ -2067,24 +1903,13 @@ def test_auto_convert_with_mixed_content():
         {
             "role": "user",
             "content": [
-                {
-                    "type": "text",
-                    "text": "What is the main topic of this legal document?"
-                },
-                {
-                    "type": "image_url",
-                    "image_url": {"url": "https://example.com/image.jpg"}
-                }
-            ]
+                {"type": "text", "text": "What is the main topic of this legal document?"},
+                {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}},
+            ],
         }
     ]
 
-    optional_params = {
-        "guardrailConfig": {
-            "guardrailIdentifier": "gr-abc123",
-            "guardrailVersion": "1"
-        }
-    }
+    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
 
     # Test the helper method directly
     converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
@@ -2108,23 +1933,10 @@ def test_auto_convert_in_full_transformation():
     config = AmazonConverseConfig()
 
     messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "What is the main topic of this legal document?"
-                }
-            ]
-        }
+        {"role": "user", "content": [{"type": "text", "text": "What is the main topic of this legal document?"}]}
     ]
 
-    optional_params = {
-        "guardrailConfig": {
-            "guardrailIdentifier": "gr-abc123",
-            "guardrailVersion": "1"
-        }
-    }
+    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
 
     # Test the full transformation
     result = config._transform_request(
@@ -2132,7 +1944,7 @@ def test_auto_convert_in_full_transformation():
         messages=messages,
         optional_params=optional_params,
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # Verify the transformation worked
@@ -2152,45 +1964,13 @@ def test_convert_consecutive_user_messages_to_guarded_text():
     config = AmazonConverseConfig()
 
     messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "First user message"
-                }
-            ]
-        },
-        {
-            "role": "assistant",
-            "content": "Assistant response"
-        },
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "Second user message"
-                }
-            ]
-        },
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "Third user message"
-                }
-            ]
-        }
+        {"role": "user", "content": [{"type": "text", "text": "First user message"}]},
+        {"role": "assistant", "content": "Assistant response"},
+        {"role": "user", "content": [{"type": "text", "text": "Second user message"}]},
+        {"role": "user", "content": [{"type": "text", "text": "Third user message"}]},
     ]
 
-    optional_params = {
-        "guardrailConfig": {
-            "guardrailIdentifier": "gr-abc123",
-            "guardrailVersion": "1"
-        }
-    }
+    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
 
     # Test the helper method directly
     converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
@@ -2223,41 +2003,12 @@ def test_convert_all_user_messages_when_all_consecutive():
     config = AmazonConverseConfig()
 
     messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "First user message"
-                }
-            ]
-        },
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "Second user message"
-                }
-            ]
-        },
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "Third user message"
-                }
-            ]
-        }
+        {"role": "user", "content": [{"type": "text", "text": "First user message"}]},
+        {"role": "user", "content": [{"type": "text", "text": "Second user message"}]},
+        {"role": "user", "content": [{"type": "text", "text": "Third user message"}]},
     ]
 
-    optional_params = {
-        "guardrailConfig": {
-            "guardrailIdentifier": "gr-abc123",
-            "guardrailVersion": "1"
-        }
-    }
+    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
 
     # Test the helper method directly
     converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
@@ -2279,26 +2030,12 @@ def test_convert_consecutive_user_messages_with_string_content():
     config = AmazonConverseConfig()
 
     messages = [
-        {
-            "role": "assistant",
-            "content": "Assistant response"
-        },
-        {
-            "role": "user",
-            "content": "First user message"
-        },
-        {
-            "role": "user",
-            "content": "Second user message"
-        }
+        {"role": "assistant", "content": "Assistant response"},
+        {"role": "user", "content": "First user message"},
+        {"role": "user", "content": "Second user message"},
     ]
 
-    optional_params = {
-        "guardrailConfig": {
-            "guardrailIdentifier": "gr-abc123",
-            "guardrailVersion": "1"
-        }
-    }
+    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
 
     # Test the helper method directly
     converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
@@ -2327,32 +2064,11 @@ def test_skip_consecutive_user_messages_with_existing_guarded_text():
     config = AmazonConverseConfig()
 
     messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "guarded_text",
-                    "text": "Already guarded"
-                }
-            ]
-        },
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "text",
-                    "text": "Should be converted"
-                }
-            ]
-        }
+        {"role": "user", "content": [{"type": "guarded_text", "text": "Already guarded"}]},
+        {"role": "user", "content": [{"type": "text", "text": "Should be converted"}]},
     ]
 
-    optional_params = {
-        "guardrailConfig": {
-            "guardrailIdentifier": "gr-abc123",
-            "guardrailVersion": "1"
-        }
-    }
+    optional_params = {"guardrailConfig": {"guardrailIdentifier": "gr-abc123", "guardrailVersion": "1"}}
 
     # Test the helper method directly
     converted_messages = config._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
@@ -2384,11 +2100,7 @@ def test_request_metadata_transformation():
     """Test that requestMetadata is properly transformed to top-level field."""
     config = AmazonConverseConfig()
 
-    request_metadata = {
-        "cost_center": "engineering",
-        "user_id": "user123",
-        "session_id": "sess_abc123"
-    }
+    request_metadata = {"cost_center": "engineering", "user_id": "user123", "session_id": "sess_abc123"}
 
     messages = [
         {"role": "user", "content": "Hello!"},
@@ -2400,7 +2112,7 @@ def test_request_metadata_transformation():
         messages=messages,
         optional_params={"requestMetadata": request_metadata},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # Verify that requestMetadata appears as top-level field
@@ -2426,7 +2138,7 @@ def test_request_metadata_validation():
         messages=messages,
         optional_params={"requestMetadata": valid_metadata},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # Test too many items (max 16)
@@ -2438,7 +2150,7 @@ def test_request_metadata_validation():
             messages=messages,
             optional_params={"requestMetadata": too_many_items},
             litellm_params={},
-            headers={}
+            headers={},
         )
         assert False, "Should have raised validation error for too many items"
     except Exception as e:
@@ -2461,7 +2173,7 @@ def test_request_metadata_key_constraints():
             messages=messages,
             optional_params={"requestMetadata": invalid_metadata},
             litellm_params={},
-            headers={}
+            headers={},
         )
         assert False, "Should have raised validation error for key too long"
     except Exception as e:
@@ -2476,7 +2188,7 @@ def test_request_metadata_key_constraints():
             messages=messages,
             optional_params={"requestMetadata": invalid_metadata},
             litellm_params={},
-            headers={}
+            headers={},
         )
         assert False, "Should have raised validation error for empty key"
     except Exception as e:
@@ -2499,7 +2211,7 @@ def test_request_metadata_value_constraints():
             messages=messages,
             optional_params={"requestMetadata": invalid_metadata},
             litellm_params={},
-            headers={}
+            headers={},
         )
         assert False, "Should have raised validation error for value too long"
     except Exception as e:
@@ -2514,7 +2226,7 @@ def test_request_metadata_value_constraints():
         messages=messages,
         optional_params={"requestMetadata": valid_metadata},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
 
@@ -2537,7 +2249,7 @@ def test_request_metadata_character_pattern():
         messages=messages,
         optional_params={"requestMetadata": valid_metadata},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
 
@@ -2545,10 +2257,7 @@ def test_request_metadata_with_other_params():
     """Test that requestMetadata works alongside other parameters."""
     config = AmazonConverseConfig()
 
-    request_metadata = {
-        "experiment": "test_A",
-        "user_type": "premium"
-    }
+    request_metadata = {"experiment": "test_A", "user_type": "premium"}
 
     messages = [
         {"role": "user", "content": "What's the weather?"},
@@ -2562,12 +2271,10 @@ def test_request_metadata_with_other_params():
                 "description": "Get the current weather",
                 "parameters": {
                     "type": "object",
-                    "properties": {
-                        "location": {"type": "string"}
-                    },
-                    "required": ["location"]
-                }
-            }
+                    "properties": {"location": {"type": "string"}},
+                    "required": ["location"],
+                },
+            },
         }
     ]
 
@@ -2575,14 +2282,9 @@ def test_request_metadata_with_other_params():
     request_data = config.transform_request(
         model="anthropic.claude-3-5-sonnet-20240620-v1:0",
         messages=messages,
-        optional_params={
-            "requestMetadata": request_metadata,
-            "tools": tools,
-            "max_tokens": 100,
-            "temperature": 0.7
-        },
+        optional_params={"requestMetadata": request_metadata, "tools": tools, "max_tokens": 100, "temperature": 0.7},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # Verify requestMetadata is at top level
@@ -2607,7 +2309,7 @@ def test_request_metadata_empty():
         messages=messages,
         optional_params={"requestMetadata": {}},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     assert "requestMetadata" in request_data
@@ -2626,7 +2328,7 @@ def test_request_metadata_not_provided():
         messages=messages,
         optional_params={},
         litellm_params={},
-        headers={}
+        headers={},
     )
 
     # requestMetadata should not be in the request
@@ -2649,16 +2351,14 @@ def test_empty_assistant_message_handling():
     messages = [
         {"role": "user", "content": "Hello"},
         {"role": "assistant", "content": ""},  # Empty content
-        {"role": "user", "content": "How are you?"}
+        {"role": "user", "content": "How are you?"},
     ]
 
     # Use patch to ensure we modify the litellm reference that factory.py actually uses
     # This avoids issues with module reloading during parallel test execution
     with patch.object(factory_module.litellm, "modify_params", True):
         result = _bedrock_converse_messages_pt(
-            messages=messages,
-            model="anthropic.claude-3-5-sonnet-20240620-v1:0",
-            llm_provider="bedrock_converse"
+            messages=messages, model="anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
         )
 
         # Should have 3 messages: user, assistant (with placeholder), user
@@ -2676,13 +2376,11 @@ def test_empty_assistant_message_handling():
         messages = [
             {"role": "user", "content": "Hello"},
             {"role": "assistant", "content": "   "},  # Whitespace-only content
-            {"role": "user", "content": "How are you?"}
+            {"role": "user", "content": "How are you?"},
         ]
 
         result = _bedrock_converse_messages_pt(
-            messages=messages,
-            model="anthropic.claude-3-5-sonnet-20240620-v1:0",
-            llm_provider="bedrock_converse"
+            messages=messages, model="anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
         )
 
         # Assistant message should have placeholder text instead of whitespace
@@ -2693,13 +2391,11 @@ def test_empty_assistant_message_handling():
         messages = [
             {"role": "user", "content": "Hello"},
             {"role": "assistant", "content": [{"type": "text", "text": ""}]},  # Empty text in list
-            {"role": "user", "content": "How are you?"}
+            {"role": "user", "content": "How are you?"},
         ]
 
         result = _bedrock_converse_messages_pt(
-            messages=messages,
-            model="anthropic.claude-3-5-sonnet-20240620-v1:0",
-            llm_provider="bedrock_converse"
+            messages=messages, model="anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
         )
 
         # Assistant message should have placeholder text instead of empty text
@@ -2710,13 +2406,11 @@ def test_empty_assistant_message_handling():
         messages = [
             {"role": "user", "content": "Hello"},
             {"role": "assistant", "content": "I'm doing well, thank you!"},  # Normal content
-            {"role": "user", "content": "How are you?"}
+            {"role": "user", "content": "How are you?"},
         ]
 
         result = _bedrock_converse_messages_pt(
-            messages=messages,
-            model="anthropic.claude-3-5-sonnet-20240620-v1:0",
-            llm_provider="bedrock_converse"
+            messages=messages, model="anthropic.claude-3-5-sonnet-20240620-v1:0", llm_provider="bedrock_converse"
         )
 
         # Assistant message should keep original content
@@ -2828,6 +2522,7 @@ def test_thinking_with_max_completion_tokens():
     assert result["thinking"]["type"] == "enabled"
     assert result["thinking"]["budget_tokens"] == 5000
 
+
 def test_drop_thinking_param_when_thinking_blocks_missing():
     """
     Test that thinking param is dropped when modify_params=True and
@@ -2868,24 +2563,21 @@ def test_drop_thinking_param_when_thinking_blocks_missing():
         optional_params = {"thinking": {"type": "enabled", "budget_tokens": 1000}}
 
         # Verify the condition is detected
-        assert last_assistant_with_tool_calls_has_no_thinking_blocks(
-            messages_without_thinking_blocks
-        ), "Should detect missing thinking_blocks"
+        assert last_assistant_with_tool_calls_has_no_thinking_blocks(messages_without_thinking_blocks), (
+            "Should detect missing thinking_blocks"
+        )
 
         # Simulate what _transform_request_helper does
         if (
             optional_params.get("thinking") is not None
             and messages_without_thinking_blocks is not None
-            and last_assistant_with_tool_calls_has_no_thinking_blocks(
-                messages_without_thinking_blocks
-            )
+            and last_assistant_with_tool_calls_has_no_thinking_blocks(messages_without_thinking_blocks)
         ):
             if litellm.modify_params:
                 optional_params.pop("thinking", None)
 
         assert "thinking" not in optional_params, (
-            "thinking param should be dropped when modify_params=True "
-            "and thinking_blocks are missing"
+            "thinking param should be dropped when modify_params=True and thinking_blocks are missing"
         )
 
         # Test case 2: thinking should NOT be dropped when thinking_blocks are present
@@ -2901,29 +2593,23 @@ def test_drop_thinking_param_when_thinking_blocks_missing():
                         "function": {"name": "search", "arguments": "{}"},
                     }
                 ],
-                "thinking_blocks": [
-                    {"type": "thinking", "thinking": "Let me search for weather..."}
-                ],
+                "thinking_blocks": [{"type": "thinking", "thinking": "Let me search for weather..."}],
             },
             {"role": "tool", "content": "Weather is sunny", "tool_call_id": "call_123"},
         ]
 
-        optional_params_with_thinking = {
-            "thinking": {"type": "enabled", "budget_tokens": 1000}
-        }
+        optional_params_with_thinking = {"thinking": {"type": "enabled", "budget_tokens": 1000}}
 
         # Verify the condition is NOT detected when thinking_blocks are present
-        assert not last_assistant_with_tool_calls_has_no_thinking_blocks(
-            messages_with_thinking_blocks
-        ), "Should NOT detect missing thinking_blocks when they are present"
+        assert not last_assistant_with_tool_calls_has_no_thinking_blocks(messages_with_thinking_blocks), (
+            "Should NOT detect missing thinking_blocks when they are present"
+        )
 
         # Simulate what _transform_request_helper does
         if (
             optional_params_with_thinking.get("thinking") is not None
             and messages_with_thinking_blocks is not None
-            and last_assistant_with_tool_calls_has_no_thinking_blocks(
-                messages_with_thinking_blocks
-            )
+            and last_assistant_with_tool_calls_has_no_thinking_blocks(messages_with_thinking_blocks)
         ):
             if litellm.modify_params:
                 optional_params_with_thinking.pop("thinking", None)
@@ -2935,24 +2621,18 @@ def test_drop_thinking_param_when_thinking_blocks_missing():
         # Test case 3: thinking should NOT be dropped when modify_params=False
         litellm.modify_params = False
 
-        optional_params_no_modify = {
-            "thinking": {"type": "enabled", "budget_tokens": 1000}
-        }
+        optional_params_no_modify = {"thinking": {"type": "enabled", "budget_tokens": 1000}}
 
         # Simulate what _transform_request_helper does
         if (
             optional_params_no_modify.get("thinking") is not None
             and messages_without_thinking_blocks is not None
-            and last_assistant_with_tool_calls_has_no_thinking_blocks(
-                messages_without_thinking_blocks
-            )
+            and last_assistant_with_tool_calls_has_no_thinking_blocks(messages_without_thinking_blocks)
         ):
             if litellm.modify_params:
                 optional_params_no_modify.pop("thinking", None)
 
-        assert "thinking" in optional_params_no_modify, (
-            "thinking param should NOT be dropped when modify_params=False"
-        )
+        assert "thinking" in optional_params_no_modify, "thinking param should NOT be dropped when modify_params=False"
 
     finally:
         # Restore original modify_params setting
@@ -2960,46 +2640,55 @@ def test_drop_thinking_param_when_thinking_blocks_missing():
 
 
 def test_supports_native_structured_outputs():
-    """Test model detection for native structured outputs support."""
-    config = AmazonConverseConfig()
+    """Test model detection for native structured outputs support.
 
-    # Supported models
-    assert config._supports_native_structured_outputs(
-        "anthropic.claude-sonnet-4-5-20250929-v1:0"
-    )
-    assert config._supports_native_structured_outputs(
-        "anthropic.claude-haiku-4-5-20251001-v1:0"
-    )
-    assert config._supports_native_structured_outputs(
-        "anthropic.claude-opus-4-6-v1:0"
-    )
-    assert config._supports_native_structured_outputs(
-        "eu.anthropic.claude-opus-4-5-20260101-v1:0"
-    )
-    assert config._supports_native_structured_outputs("qwen.qwen3-235b-instruct-v1:0")
-    assert config._supports_native_structured_outputs("mistral.mistral-large-3-v1:0")
-    assert config._supports_native_structured_outputs("deepseek.deepseek-v3.1-v1:0")
+    Support is driven by the ``supports_native_structured_output`` flag in the
+    cost JSON (litellm.model_cost), not a hardcoded model set.
+    """
+    old_env = os.environ.get("LITELLM_LOCAL_MODEL_COST_MAP")
+    old_cost = litellm.model_cost
+    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+    try:
+        config = AmazonConverseConfig()
 
-    # Unsupported models — should fall back to tool-call approach
-    assert not config._supports_native_structured_outputs(
-        "anthropic.claude-3-5-sonnet-20241022-v2:0"
-    )
-    assert not config._supports_native_structured_outputs(
-        "anthropic.claude-sonnet-4-20250514-v1:0"
-    )
-    assert not config._supports_native_structured_outputs(
-        "meta.llama3-3-70b-instruct-v1:0"
-    )
-    assert not config._supports_native_structured_outputs(
-        "amazon.nova-pro-v1:0"
-    )
-    # Excluded despite AWS listing them: broken constrained decoding on Bedrock
-    assert not config._supports_native_structured_outputs(
-        "openai.gpt-oss-120b-1:0"
-    )
-    assert not config._supports_native_structured_outputs(
-        "mistral.magistral-small-2509"
-    )
+        # Supported models (have supports_native_structured_output=true in cost JSON)
+        assert config._supports_native_structured_outputs("anthropic.claude-sonnet-4-5-20250929-v1:0")
+        assert config._supports_native_structured_outputs("anthropic.claude-haiku-4-5-20251001-v1:0")
+        assert config._supports_native_structured_outputs("anthropic.claude-opus-4-6-v1")
+        # Version suffix (:0) is stripped when looking up models without it in cost JSON
+        assert config._supports_native_structured_outputs("anthropic.claude-opus-4-6-v1:0")
+        # Regional prefix is stripped by get_bedrock_base_model
+        assert config._supports_native_structured_outputs("eu.anthropic.claude-opus-4-5-20251101-v1:0")
+        # Claude 4.6 Sonnet
+        assert config._supports_native_structured_outputs("anthropic.claude-sonnet-4-6")
+        assert config._supports_native_structured_outputs("us.anthropic.claude-sonnet-4-6")
+        # Non-Anthropic models
+        assert config._supports_native_structured_outputs("qwen.qwen3-235b-a22b-2507-v1:0")
+        assert config._supports_native_structured_outputs("mistral.mistral-large-3-675b-instruct")
+        assert config._supports_native_structured_outputs("minimax.minimax-m2")
+        assert config._supports_native_structured_outputs("moonshot.kimi-k2-thinking")
+        assert config._supports_native_structured_outputs("nvidia.nemotron-nano-3-30b")
+        # DeepSeek: old substring "deepseek-v3.1" didn't match real ID
+        assert config._supports_native_structured_outputs("deepseek.v3-v1:0")
+
+        # Unsupported models -- should fall back to tool-call approach
+        assert not config._supports_native_structured_outputs("anthropic.claude-3-5-sonnet-20241022-v2:0")
+        assert not config._supports_native_structured_outputs("anthropic.claude-sonnet-4-20250514-v1:0")
+        assert not config._supports_native_structured_outputs("meta.llama3-3-70b-instruct-v1:0")
+        assert not config._supports_native_structured_outputs("amazon.nova-pro-v1:0")
+        # Excluded: broken constrained decoding on Bedrock
+        assert not config._supports_native_structured_outputs("openai.gpt-oss-120b-1:0")
+        assert not config._supports_native_structured_outputs("mistral.magistral-small-2509")
+        # Excluded: ignores schema or broken on Bedrock
+        assert not config._supports_native_structured_outputs("google.gemma-3-27b-it")
+        assert not config._supports_native_structured_outputs("nvidia.nemotron-nano-12b-v2")
+    finally:
+        litellm.model_cost = old_cost
+        if old_env is None:
+            os.environ.pop("LITELLM_LOCAL_MODEL_COST_MAP", None)
+        else:
+            os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = old_env
 
 
 def test_create_output_config_for_response_format():
@@ -3078,10 +2767,7 @@ def test_translate_response_format_native_output_config():
     parsed_schema = json.loads(schema_str)
     expected_schema = {**response_format["json_schema"]["schema"], "additionalProperties": False}
     assert parsed_schema == expected_schema
-    assert (
-        result["outputConfig"]["textFormat"]["structure"]["jsonSchema"]["name"]
-        == "WeatherResult"
-    )
+    assert result["outputConfig"]["textFormat"]["structure"]["jsonSchema"]["name"] == "WeatherResult"
 
 
 def test_translate_response_format_fallback_tool_call():
@@ -3118,42 +2804,53 @@ def test_translate_response_format_fallback_tool_call():
 
 def test_native_structured_output_no_fake_stream():
     """When using native structured outputs with streaming, fake_stream should NOT be set."""
-    config = AmazonConverseConfig()
+    old_env = os.environ.get("LITELLM_LOCAL_MODEL_COST_MAP")
+    old_cost = litellm.model_cost
+    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+    try:
+        config = AmazonConverseConfig()
 
-    response_format = {
-        "type": "json_schema",
-        "json_schema": {
-            "name": "Result",
-            "schema": {
-                "type": "object",
-                "properties": {
-                    "answer": {"type": "string"},
+        response_format = {
+            "type": "json_schema",
+            "json_schema": {
+                "name": "Result",
+                "schema": {
+                    "type": "object",
+                    "properties": {
+                        "answer": {"type": "string"},
+                    },
                 },
             },
-        },
-    }
-
-    optional_params: dict = {}
-    result = config._translate_response_format_param(
-        value=response_format,
-        model="anthropic.claude-sonnet-4-5-20250929-v1:0",
-        optional_params=optional_params,
-        non_default_params={"response_format": response_format, "stream": True},
-        is_thinking_enabled=False,
-    )
+        }
 
-    assert "outputConfig" in result
-    assert result["json_mode"] is True
-    # No fake_stream for native approach
-    assert "fake_stream" not in result
+        optional_params: dict = {}
+        result = config._translate_response_format_param(
+            value=response_format,
+            model="anthropic.claude-sonnet-4-5-20250929-v1:0",
+            optional_params=optional_params,
+            non_default_params={"response_format": response_format, "stream": True},
+            is_thinking_enabled=False,
+        )
 
-    # Verify the schema content
-    schema_str = result["outputConfig"]["textFormat"]["structure"]["jsonSchema"]["schema"]
-    assert json.loads(schema_str) == {
-        "type": "object",
-        "properties": {"answer": {"type": "string"}},
-        "additionalProperties": False,
-    }
+        assert "outputConfig" in result
+        assert result["json_mode"] is True
+        # No fake_stream for native approach
+        assert "fake_stream" not in result
+
+        # Verify the schema content
+        schema_str = result["outputConfig"]["textFormat"]["structure"]["jsonSchema"]["schema"]
+        assert json.loads(schema_str) == {
+            "type": "object",
+            "properties": {"answer": {"type": "string"}},
+            "additionalProperties": False,
+        }
+    finally:
+        litellm.model_cost = old_cost
+        if old_env is None:
+            os.environ.pop("LITELLM_LOCAL_MODEL_COST_MAP", None)
+        else:
+            os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = old_env
 
 
 def test_transform_request_with_output_config():
@@ -3227,11 +2924,7 @@ def test_transform_response_native_structured_output():
         "output": {
             "message": {
                 "role": "assistant",
-                "content": [
-                    {
-                        "text": '{"temp": 62, "description": "Mild and foggy"}'
-                    }
-                ],
+                "content": [{"text": '{"temp": 62, "description": "Mild and foggy"}'}],
             }
         },
         "stopReason": "end_turn",
@@ -3388,23 +3081,34 @@ def test_add_additional_properties_definitions():
 def test_json_object_no_schema_falls_back_to_tool_call():
     """response_format: {type: json_object} with no schema should use tool-call fallback,
     even for models that support native structured outputs."""
-    config = AmazonConverseConfig()
-    optional_params: dict = {}
-    non_default_params = {"response_format": {"type": "json_object"}}
+    old_env = os.environ.get("LITELLM_LOCAL_MODEL_COST_MAP")
+    old_cost = litellm.model_cost
+    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+    try:
+        config = AmazonConverseConfig()
+        optional_params: dict = {}
+        non_default_params = {"response_format": {"type": "json_object"}}
 
-    result = config._translate_response_format_param(
-        value=non_default_params["response_format"],
-        model="anthropic.claude-sonnet-4-5-20250929-v1:0",
-        optional_params=optional_params,
-        non_default_params=non_default_params,
-        is_thinking_enabled=False,
-    )
+        result = config._translate_response_format_param(
+            value=non_default_params["response_format"],
+            model="anthropic.claude-sonnet-4-5-20250929-v1:0",
+            optional_params=optional_params,
+            non_default_params=non_default_params,
+            is_thinking_enabled=False,
+        )
 
-    # Should NOT use native outputConfig (no schema provided)
-    assert "outputConfig" not in result
-    # Should use tool-call fallback
-    assert "tools" in result
-    assert result["json_mode"] is True
+        # Should NOT use native outputConfig (no schema provided)
+        assert "outputConfig" not in result
+        # Should use tool-call fallback
+        assert "tools" in result
+        assert result["json_mode"] is True
+    finally:
+        litellm.model_cost = old_cost
+        if old_env is None:
+            os.environ.pop("LITELLM_LOCAL_MODEL_COST_MAP", None)
+        else:
+            os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = old_env
 
 
 def test_output_config_applies_additional_properties():
@@ -3427,7 +3131,6 @@ def test_output_config_applies_additional_properties():
     assert parsed["properties"]["nested"]["additionalProperties"] is False
 
 
-
 _TOOL_PARAM = [
     {
         "type": "function",
@@ -3505,9 +3208,7 @@ def test_parallel_tool_calls_older_model_drops_disable_flag():
 class TestBedrockMinThinkingBudgetTokens:
     """Test that thinking.budget_tokens is clamped to the Bedrock minimum (1024)."""
 
-    def _map_params(
-        self, thinking_value, model="anthropic.claude-3-7-sonnet-20250219-v1:0"
-    ):
+    def _map_params(self, thinking_value, model="anthropic.claude-3-7-sonnet-20250219-v1:0"):
         """Helper to call map_openai_params with the given thinking value."""
         config = AmazonConverseConfig()
         non_default_params = {"thinking": thinking_value}
@@ -3545,6 +3246,7 @@ def test_no_thinking_param_does_not_error(self):
         )
         assert "thinking" not in result or result.get("thinking") is None
 
+
 def test_transform_response_with_both_json_tool_call_and_real_tool():
     """
     When Bedrock returns BOTH json_tool_call AND a real tool (get_weather),
@@ -3733,9 +3435,7 @@ def test_streaming_filters_json_tool_call_with_real_tools():
 
     # Chunk 2: json_tool_call delta — should become text, not tool_use
     json_delta = ContentBlockDeltaEvent(toolUse={"input": '{"temp": 62}'})
-    text_2, tool_use_2, _, _, _ = decoder._handle_converse_delta_event(
-        json_delta, index=0
-    )
+    text_2, tool_use_2, _, _, _ = decoder._handle_converse_delta_event(json_delta, index=0)
     assert text_2 == '{"temp": 62}'
     assert tool_use_2 is None
 
@@ -3758,12 +3458,8 @@ def test_streaming_filters_json_tool_call_with_real_tools():
     assert decoder.tool_calls_index == 0
 
     # Chunk 5: real tool delta
-    real_delta = ContentBlockDeltaEvent(
-        toolUse={"input": '{"location": "SF"}'}
-    )
-    text_5, tool_use_5, _, _, _ = decoder._handle_converse_delta_event(
-        real_delta, index=1
-    )
+    real_delta = ContentBlockDeltaEvent(toolUse={"input": '{"location": "SF"}'})
+    text_5, tool_use_5, _, _, _ = decoder._handle_converse_delta_event(real_delta, index=1)
     assert text_5 == ""
     assert tool_use_5 is not None
     assert tool_use_5["function"]["arguments"] == '{"location": "SF"}'
@@ -3796,10 +3492,7 @@ def test_streaming_without_json_mode_passes_all_tools():
 
     # json_tool_call delta — should be a tool_use, not text
     json_delta = ContentBlockDeltaEvent(toolUse={"input": '{"data": 1}'})
-    text, tool_use_delta, _, _, _ = decoder._handle_converse_delta_event(
-        json_delta, index=0
-    )
+    text, tool_use_delta, _, _, _ = decoder._handle_converse_delta_event(json_delta, index=0)
     assert text == ""
     assert tool_use_delta is not None
     assert tool_use_delta["function"]["arguments"] == '{"data": 1}'
-