fix(passthrough): resolve costing model when body model is unknown (#30160)

(cherry picked from commit 1828a7c6f0)
This commit is contained in:
Yassin Kortam 2026-06-11 14:26:55 -07:00 committed by Yuneng Jiang
parent 7193461559
commit 45d467cb0c
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@ -100,6 +100,42 @@ class AnthropicPassthroughLoggingHandler:
return get_end_user_id_from_request_body(request_body)
return None
@staticmethod
def _resolve_costing_model(model: str, logging_obj: LiteLLMLoggingObj) -> str:
if model and model != "unknown":
return model
litellm_params = (getattr(logging_obj, "model_call_details", {}) or {}).get(
"litellm_params", {}
) or {}
deployment_model = litellm_params.get("model")
if deployment_model and deployment_model != "unknown":
return deployment_model
model_group = (litellm_params.get("metadata", {}) or {}).get("model_group")
if model_group:
return model_group.removeprefix("passthrough/")
return model
@staticmethod
def _extract_model_from_anthropic_chunks(
all_chunks: Sequence[Union[str, bytes]],
) -> Optional[str]:
for raw in all_chunks:
text = raw.decode("utf-8") if isinstance(raw, bytes) else raw
for line in text.splitlines():
if not line.startswith("data:"):
continue
try:
data = json.loads(line[len("data:") :].strip())
except (json.JSONDecodeError, ValueError):
continue
if not isinstance(data, dict):
continue
if data.get("type") == "message_start":
model = (data.get("message") or {}).get("model")
if model:
return model
return None
@staticmethod
def _create_anthropic_response_logging_payload(
litellm_model_response: Union[ModelResponse, TextCompletionResponse],
@ -127,6 +163,10 @@ class AnthropicPassthroughLoggingHandler:
"custom_llm_provider"
)
model = AnthropicPassthroughLoggingHandler._resolve_costing_model(
model, logging_obj
)
# Prepend custom_llm_provider to model if not already present
model_for_cost = model
if custom_llm_provider and not model.startswith(f"{custom_llm_provider}/"):
@ -213,6 +253,15 @@ class AnthropicPassthroughLoggingHandler:
):
model = cast(str, litellm_logging_obj.model_call_details.get("model"))
if not model or model == "unknown":
chunk_model = (
AnthropicPassthroughLoggingHandler._extract_model_from_anthropic_chunks(
all_chunks
)
)
if chunk_model:
model = chunk_model
complete_streaming_response = (
AnthropicPassthroughLoggingHandler._build_complete_streaming_response(
all_chunks=all_chunks,