fix(logging): backfill streaming hidden response cost

litellm/litellm_core_utils/litellm_logging.py

@@ -1725,12 +1725,18 @@ def _merge_hidden_params_from_response_into_metadata(
             return
         if self.model_call_details.get("litellm_params") is None:
             return
-        self.model_call_details["litellm_params"].setdefault("metadata", {})
-        if self.model_call_details["litellm_params"]["metadata"] is None:
-            self.model_call_details["litellm_params"]["metadata"] = {}
-        self.model_call_details["litellm_params"]["metadata"]["hidden_params"] = (
-            getattr(logging_result, "_hidden_params", {})
-        )
+        metadata_hidden_params = hidden_params.copy()
+        response_cost = self.model_call_details.get("response_cost")
+        if (
+            metadata_hidden_params.get("response_cost") is None
+            and response_cost is not None
+        ):
+            metadata_hidden_params["response_cost"] = response_cost
+
+        litellm_params = self.model_call_details["litellm_params"]
+        metadata = litellm_params.get("metadata") or {}
+        litellm_params["metadata"] = metadata
+        metadata["hidden_params"] = metadata_hidden_params
 
     def _process_hidden_params_and_response_cost(
         self,
@@ -5438,11 +5444,6 @@ def get_standard_logging_object_payload(
             completion_start_time_float=completion_start_time_float,
             stream=kwargs.get("stream", False),
         )
-        # clean up litellm hidden params
-        clean_hidden_params = StandardLoggingPayloadSetup.get_hidden_params(
-            hidden_params
-        )
-
         # clean up litellm metadata
         clean_metadata = StandardLoggingPayloadSetup.get_standard_logging_metadata(
             metadata=metadata,
@@ -5476,6 +5477,18 @@ def get_standard_logging_object_payload(
         ## Get model cost information ##
         base_model = _get_base_model_from_metadata(model_call_details=kwargs)
         custom_pricing = use_custom_pricing_for_model(litellm_params=litellm_params)
+        raw_response_cost = kwargs.get("response_cost")
+        response_cost: float = raw_response_cost or 0.0
+
+        # clean up litellm hidden params
+        clean_hidden_params = StandardLoggingPayloadSetup.get_hidden_params(
+            hidden_params
+        )
+        if (
+            clean_hidden_params["response_cost"] is None
+            and raw_response_cost is not None
+        ):
+            clean_hidden_params["response_cost"] = response_cost
 
         model_cost_information = StandardLoggingPayloadSetup.get_model_cost_information(
             base_model=base_model,
@@ -5484,7 +5497,6 @@ def get_standard_logging_object_payload(
             init_response_obj=init_response_obj,
             api_base=litellm_params.get("api_base"),
         )
-        response_cost: float = kwargs.get("response_cost", 0) or 0.0
 
         error_information = StandardLoggingPayloadSetup.get_error_information(
             original_exception=original_exception,

litellm/types/utils.py

@@ -2659,7 +2659,7 @@ class StandardLoggingHiddenParams(TypedDict):
     ]  # id of the model in the router, separates multiple models with the same name but different credentials
     cache_key: Optional[str]
     api_base: Optional[str]
-    response_cost: Optional[str]
+    response_cost: Optional[Union[str, float]]
     litellm_overhead_time_ms: Optional[float]
     additional_headers: Optional[StandardLoggingAdditionalHeaders]
     batch_models: Optional[List[str]]

tests/test_litellm/litellm_core_utils/test_litellm_logging.py

@@ -2337,6 +2337,104 @@ class _Resp:
     assert meta["hidden_params"]["model_id"] == "mid-test"
 
 
+def test_merge_hidden_params_from_response_into_metadata_backfills_response_cost():
+    """Streaming metadata should include the already-calculated response cost."""
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+
+    logging_obj = LiteLLMLoggingObj(
+        model="gpt-4o-mini",
+        messages=[{"role": "user", "content": "hi"}],
+        stream=True,
+        call_type="acompletion",
+        start_time=time.time(),
+        litellm_call_id="merge-hp-cost-test",
+        function_id="merge-hp-cost-fn",
+    )
+    logging_obj.model_call_details = {
+        "litellm_params": {"metadata": {}},
+        "response_cost": 0.002,
+    }
+
+    class _Resp:
+        _hidden_params = {"response_cost": None, "model_id": "mid-test"}
+
+    response = _Resp()
+    logging_obj._merge_hidden_params_from_response_into_metadata(response)
+    meta = logging_obj.model_call_details["litellm_params"]["metadata"]
+    assert meta["hidden_params"]["response_cost"] == 0.002
+    assert meta["hidden_params"]["model_id"] == "mid-test"
+    assert response._hidden_params["response_cost"] is None
+
+
+def test_standard_logging_hidden_params_backfills_response_cost_without_mutating_response():
+    """Streaming standard logging payload should expose the calculated response cost."""
+    from datetime import datetime
+
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+    from litellm.types.utils import Usage
+
+    logging_obj = LiteLLMLoggingObj(
+        model="gpt-4o-mini",
+        messages=[{"role": "user", "content": "hi"}],
+        stream=True,
+        call_type="acompletion",
+        start_time=time.time(),
+        litellm_call_id="standard-hp-cost-test",
+        function_id="standard-hp-cost-fn",
+    )
+    logging_obj.model_call_details = {
+        "litellm_params": {"metadata": {}, "proxy_server_request": {}},
+        "litellm_call_id": "standard-hp-cost-test",
+        "call_type": "acompletion",
+        "stream": True,
+        "model": "gpt-4o-mini",
+        "custom_llm_provider": "openai",
+        "optional_params": {"stream": True},
+        "response_cost": 0.002,
+    }
+    response = ModelResponse(
+        id="standard-hp-cost-response",
+        model="gpt-4o-mini",
+        usage=Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15),
+    )
+    response._hidden_params = {"response_cost": None, "model_id": "mid-test"}
+
+    payload = logging_obj._build_standard_logging_payload(
+        response, datetime.now(), datetime.now()
+    )
+
+    assert payload is not None
+    assert payload["hidden_params"]["response_cost"] == 0.002
+    assert response._hidden_params["response_cost"] is None
+
+
+def test_merge_hidden_params_from_response_into_metadata_preserves_response_cost():
+    """Do not overwrite provider-supplied response cost when it already exists."""
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+
+    logging_obj = LiteLLMLoggingObj(
+        model="gpt-4o-mini",
+        messages=[{"role": "user", "content": "hi"}],
+        stream=True,
+        call_type="acompletion",
+        start_time=time.time(),
+        litellm_call_id="merge-hp-preserve-cost-test",
+        function_id="merge-hp-preserve-cost-fn",
+    )
+    logging_obj.model_call_details = {
+        "litellm_params": {"metadata": {}},
+        "response_cost": 0.002,
+    }
+
+    class _Resp:
+        _hidden_params = {"response_cost": 0.001, "model_id": "mid-test"}
+
+    logging_obj._merge_hidden_params_from_response_into_metadata(_Resp())
+    meta = logging_obj.model_call_details["litellm_params"]["metadata"]
+    assert meta["hidden_params"]["response_cost"] == 0.001
+    assert meta["hidden_params"]["model_id"] == "mid-test"
+
+
 def test_merge_hidden_params_from_response_into_metadata_no_op_when_empty():
     from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj