diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py
index 82260eb2ae5..ffef4a2a865 100644
--- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py
+++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py
@@ -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,