fix(logging): convert Usage object to dict in non-streaming /v1/responses transform

_transform_usage_objects was setattr-ing a raw Usage Pydantic object
instead of a dict when setting result.usage for non-streaming
ResponsesAPIResponse results. Since ResponsesAPIResponse.usage is typed
as ResponseAPIUsage (which lacks prompt_tokens/completion_tokens),
model_dump() would silently drop those fields, corrupting token usage
reporting for non-streaming /v1/responses calls. The
standard_logging_payload branch already converted to dict correctly;
this applies the same pattern to the result.usage assignment itself.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

litellm/litellm_core_utils/litellm_logging.py

@@ -2686,17 +2686,13 @@ class Logging(LiteLLMLoggingBaseClass):
     ) -> object:
         if isinstance(result, ResponsesAPIResponse):
             result = result.model_copy()
-            transformed_usage = ResponseAPILoggingUtils.transform_response_api_usage_to_chat_usage(result.usage)
-            setattr(result, "usage", transformed_usage)
+            chat_usage: Final = ResponseAPILoggingUtils.transform_response_api_usage_to_chat_usage(result.usage)
+            chat_usage_dict: Final = chat_usage.model_dump() if hasattr(chat_usage, "model_dump") else dict(chat_usage)
+            setattr(result, "usage", chat_usage_dict)
             if standard_logging_payload is not None:
                 response_dict: Final = result.model_dump() if hasattr(result, "model_dump") else dict(result)
-                # Ensure usage is properly included with transformed chat format
-                if transformed_usage is not None:
-                    response_dict["usage"] = (
-                        transformed_usage.model_dump()
-                        if hasattr(transformed_usage, "model_dump")
-                        else dict(transformed_usage)
-                    )
+                if chat_usage is not None:
+                    response_dict["usage"] = chat_usage_dict
                 standard_logging_payload["response"] = response_dict  # rebind-ok: mirrors the transformed usage
         elif isinstance(result, TranscriptionResponse):
             from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import (

tests/unit/litellm_core_utils/test_litellm_logging.py

@@ -6185,6 +6185,37 @@ def test_success_handler_computes_cost_for_dict_response():
         assert logging_obj.model_call_details["response_cost"] == expected_cost
 
 
+def test_transform_usage_objects_non_streaming_preserves_prompt_and_completion_tokens():
+    response = ResponsesAPIResponse(
+        id="resp-test-001",
+        created_at=1700000000,
+        output=[],
+        usage=ResponseAPIUsage(input_tokens=100, output_tokens=50, total_tokens=150),
+    )
+
+    logging_obj = LitellmLogging(
+        model="gpt-5",
+        messages=[{"role": "user", "content": "Hello"}],
+        stream=False,
+        call_type="responses",
+        start_time=time.time(),
+        litellm_call_id="test-usage-preservation",
+        function_id="test-fn-usage",
+    )
+
+    result = logging_obj._transform_usage_objects(response)
+
+    assert isinstance(result.usage, dict)
+    assert result.usage.get("prompt_tokens") == 100
+    assert result.usage.get("completion_tokens") == 50
+    assert result.usage.get("total_tokens") == 150
+
+    dumped_usage = result.model_dump().get("usage", {})
+    assert dumped_usage.get("prompt_tokens") == 100
+    assert dumped_usage.get("completion_tokens") == 50
+    assert dumped_usage.get("total_tokens") == 150
+
+
 def test_success_handler_preserves_precomputed_cost_for_dict_response():
     """Precomputed response_cost on model_call_details must not be overwritten."""
     logging_obj = _make_dict_logging_obj()