fix(anthropic): report reasoning tokens in /v1/messages usage for non-anthropic models

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py

@@ -80,6 +80,7 @@ from litellm.types.llms.anthropic import (
     AnthropicMessagesRequest,
     AnthropicMessagesToolChoice,
     AnthropicMessagesUserMessageParam,
+    AnthropicOutputTokensDetails,
     AnthropicResponseContentBlockRedactedThinking,
     AnthropicResponseContentBlockText,
     AnthropicResponseContentBlockThinking,
@@ -1265,6 +1266,15 @@ class LiteLLMAnthropicMessagesAdapter:
             return explicit_value
         return cls._first_positive_prompt_tokens_detail_value(usage, ("cache_creation_tokens", "cache_write_tokens"))
 
+    @classmethod
+    def _get_reasoning_tokens(cls, usage: Usage) -> int:
+        completion_tokens_details: Final = getattr(usage, "completion_tokens_details", None)
+        if completion_tokens_details is None:
+            return 0
+        if isinstance(completion_tokens_details, dict):
+            return cls._positive_int(completion_tokens_details.get("reasoning_tokens"))
+        return cls._positive_int(getattr(completion_tokens_details, "reasoning_tokens", None))
+
     @classmethod
     def _translate_openai_usage_to_anthropic_usage_delta(cls, usage: Usage) -> UsageDelta:
         cache_read_input_tokens: Final = cls._get_cache_read_input_tokens(usage)
@@ -1282,6 +1292,9 @@ class LiteLLMAnthropicMessagesAdapter:
             usage_delta["cache_creation_input_tokens"] = cache_creation_input_tokens
         if cache_read_input_tokens > 0:
             usage_delta["cache_read_input_tokens"] = cache_read_input_tokens
+        reasoning_tokens: Final = cls._get_reasoning_tokens(usage)
+        if reasoning_tokens > 0:
+            usage_delta["output_tokens_details"] = AnthropicOutputTokensDetails(thinking_tokens=reasoning_tokens)
         return usage_delta
 
     @classmethod

litellm/types/llms/anthropic.py

@@ -486,11 +486,18 @@ class MessageDelta(TypedDict, total=False):
     stop_reason: str | None
 
 
+class AnthropicOutputTokensDetails(TypedDict, total=False):
+    """Anthropic ``usage.output_tokens_details`` (extended thinking breakdown)."""
+
+    thinking_tokens: int
+
+
 class UsageDelta(TypedDict, total=False):
     input_tokens: int
     output_tokens: int
     cache_creation_input_tokens: int
     cache_read_input_tokens: int
+    output_tokens_details: AnthropicOutputTokensDetails
 
 
 class AppliedEdit(TypedDict, total=False):

litellm/types/llms/anthropic_messages/anthropic_response.py

@@ -3,6 +3,7 @@ from typing import Any, Literal, TypeAlias
 from typing_extensions import NotRequired, TypedDict
 
 from litellm.types.llms.anthropic import (
+    AnthropicOutputTokensDetails,
     AnthropicResponseContentBlockText,
     AnthropicResponseContentBlockToolUse,
     ContextManagementResponse,
@@ -71,6 +72,11 @@ class AnthropicUsage(TypedDict, total=False):
     cache_creation_input_tokens: int
     cache_read_input_tokens: int
 
+    """
+    Extended thinking breakdown of ``output_tokens``
+    """
+    output_tokens_details: AnthropicOutputTokensDetails
+
 
 class AnthropicMessagesResponse(TypedDict, total=False):
     """

tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py

@@ -2331,6 +2331,84 @@ def test_translate_openai_usage_to_anthropic_cache_tokens_from_dict_details_with
     assert anthropic_usage["cache_creation_input_tokens"] == 20
 
 
+def test_translate_openai_usage_to_anthropic_reports_reasoning_tokens():
+    """
+    Reasoning models served through /v1/messages must report the reasoning tokens they billed for,
+    in Anthropic's own shape (usage.output_tokens_details.thinking_tokens).
+
+    Fixes: https://github.com/BerriAI/litellm/issues/36376
+    """
+    from litellm.types.utils import CompletionTokensDetailsWrapper
+
+    usage = Usage(
+        prompt_tokens=15,
+        completion_tokens=100,
+        total_tokens=115,
+        completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=68, text_tokens=32),
+    )
+
+    anthropic_usage = LiteLLMAnthropicMessagesAdapter._translate_openai_usage_to_anthropic_usage_delta(usage)
+
+    assert anthropic_usage["output_tokens"] == 100
+    assert anthropic_usage["output_tokens_details"] == {"thinking_tokens": 68}
+
+
+def test_translate_openai_usage_to_anthropic_reads_reasoning_tokens_from_dict_details():
+    usage = Usage(prompt_tokens=15, completion_tokens=100, total_tokens=115)
+    usage.completion_tokens_details = {"reasoning_tokens": 68}
+
+    anthropic_usage = LiteLLMAnthropicMessagesAdapter._translate_openai_usage_to_anthropic_usage_delta(usage)
+
+    assert anthropic_usage["output_tokens_details"] == {"thinking_tokens": 68}
+
+
+def test_translate_openai_usage_to_anthropic_omits_zero_reasoning_tokens():
+    from litellm.types.utils import CompletionTokensDetailsWrapper
+
+    usage = Usage(
+        prompt_tokens=15,
+        completion_tokens=100,
+        total_tokens=115,
+        completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=0, text_tokens=100),
+    )
+
+    anthropic_usage = LiteLLMAnthropicMessagesAdapter._translate_openai_usage_to_anthropic_usage_delta(usage)
+
+    assert "output_tokens_details" not in anthropic_usage
+
+
+def test_translate_openai_response_to_anthropic_reports_reasoning_tokens():
+    from litellm.types.utils import CompletionTokensDetailsWrapper
+
+    usage = Usage(
+        prompt_tokens=15,
+        completion_tokens=100,
+        total_tokens=115,
+        completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=68, text_tokens=32),
+    )
+    response = ModelResponse(
+        id="chatcmpl-1",
+        choices=[
+            Choices(
+                finish_reason="stop",
+                index=0,
+                message=Message(content="2", role="assistant"),
+            )
+        ],
+        created=1,
+        model="gpt-5.1",
+        object="chat.completion",
+        usage=usage,
+    )
+
+    anthropic_response = LiteLLMAnthropicMessagesAdapter().translate_openai_response_to_anthropic(
+        response=response,
+        tool_name_mapping=None,
+    )
+
+    assert anthropic_response["usage"]["output_tokens_details"] == {"thinking_tokens": 68}
+
+
 def test_translate_openai_usage_to_anthropic_ignores_fractional_cache_tokens():
     usage = Usage(
         prompt_tokens=120,