fix: repair seven regressions caught by CircleCI on main

litellm/cost_calculator.py

@@ -2375,6 +2375,11 @@ def default_image_cost_calculator(
         model_name_without_custom_llm_provider = model.replace(f"{custom_llm_provider}/", "")
         base_model_name = f"{custom_llm_provider}/{size_str}/{model_name_without_custom_llm_provider}"
     model_name_with_quality: Final = f"{quality}/{base_model_name}" if quality else base_model_name
+    provider_first_model_name_with_quality: Final = (
+        f"{custom_llm_provider}/{quality}/{size_str}/{model_name_without_custom_llm_provider or model}"
+        if quality and custom_llm_provider
+        else None
+    )
 
     # gpt-image-1 models use low, medium, high quality. If user did not specify quality, use medium fot gpt-image-1 model family
     model_name_with_v2_quality: Final = f"{ImageGenerationRequestQuality.HIGH.value}/{base_model_name}"
@@ -2386,6 +2391,7 @@ def default_image_cost_calculator(
 
     models_to_check: Final = (
         model_name_with_quality,
+        provider_first_model_name_with_quality,
         base_model_name,
         model_name_with_v2_quality,
         model_with_quality_without_provider,

litellm/llms/bedrock/chat/invoke_handler.py

@@ -231,6 +231,12 @@ async def make_call(
                 sync_stream=False,
             )
             completion_stream = decoder.aiter_bytes(response.aiter_bytes(chunk_size=stream_chunk_size))
+        elif bedrock_invoke_provider == "moonshot":
+            decoder = AmazonOpenAICompatibleStreamDecoder(
+                model=model,
+                sync_stream=False,
+            )
+            completion_stream = decoder.aiter_bytes(response.aiter_bytes(chunk_size=stream_chunk_size))
         else:
             decoder = AWSEventStreamDecoder(model=model, json_mode=json_mode)
             completion_stream = decoder.aiter_bytes(response.aiter_bytes(chunk_size=stream_chunk_size))
@@ -329,6 +335,12 @@ def make_sync_call(
                 sync_stream=True,
             )
             completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size))
+        elif bedrock_invoke_provider == "moonshot":
+            decoder = AmazonOpenAICompatibleStreamDecoder(
+                model=model,
+                sync_stream=True,
+            )
+            completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size))
         else:
             decoder = AWSEventStreamDecoder(model=model, json_mode=json_mode)
             completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size))
@@ -795,6 +807,24 @@ def _chunk_parser(self, chunk_data: dict) -> GChunk | ModelResponseStream | dict
         return self.deepseek_model_response_iterator.chunk_parser(chunk=chunk_data)
 
 
+class AmazonOpenAICompatibleStreamDecoder(AWSEventStreamDecoder):
+    def __init__(
+        self,
+        model: str,
+        sync_stream: bool,
+    ) -> None:
+        super().__init__(model=model)
+        from litellm.llms.openai.chat.gpt_transformation import OpenAIChatCompletionStreamingHandler
+
+        self.openai_model_response_iterator = OpenAIChatCompletionStreamingHandler(
+            streaming_response=None,
+            sync_stream=sync_stream,
+        )
+
+    def _chunk_parser(self, chunk_data: dict[str, object]) -> ModelResponseStream:
+        return self.openai_model_response_iterator.chunk_parser(chunk=chunk_data)
+
+
 class MockResponseIterator:  # for returning ai21 streaming responses
     def __init__(self, model_response, json_mode: bool | None = False):
         self.model_response = model_response

litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py

@@ -287,7 +287,10 @@ def _is_gemini_3_or_newer(model: str) -> bool:
         Check if the model is Gemini 3 or newer.
         """
         model_name = model.split("/")[-1].lower()
-        if not model_name:
+        is_vertex_fine_tuned_model: Final = model_name.isdigit() or (
+            model.startswith("gemini/") and not model_name.startswith("gemini-")
+        )
+        if not model_name or is_vertex_fine_tuned_model or model_name.startswith("gemma-"):
             return False
         # Pre-Gemini 3 models: gemini-1.x, gemini-2.x, gemini-pro, gemini-flash, gemini-exp
         if re.match(r"^gemini-(?:[12](?:\.\d+)?|exp|(?:pro|flash)(?!-(?:lite-)?latest$))(?:-|$)", model_name):