fix(router): don't log 'Could not identify azure model' when the deployment name resolves from the cost map

litellm/router.py

@@ -9184,10 +9184,27 @@ def get_router_model_info(
 
         ## SET MODEL TO 'model=' - if base_model is None + not azure
         if custom_llm_provider == "azure" and base_model is None:
-            verbose_router_logger.error(
-                "Could not identify azure model '%s'. Set azure 'base_model' for accurate max tokens, cost tracking, etc.- https://docs.litellm.ai/docs/proxy/cost_tracking#spend-tracking-for-azure-openai-models",
-                _model,
-            )
+            # Router init auto-registers every deployment name into
+            # litellm.model_cost as a zeroed stub, so membership alone can't
+            # tell a resolvable name apart; require usable limits/costs.
+            _azure_fallback_key = _model if _model.startswith("azure/") else f"azure/{_model}"
+            _fallback_entry = litellm.model_cost.get(_azure_fallback_key)
+            _fallback_resolves = _fallback_entry is not None and (
+                (_fallback_entry.get("max_input_tokens") or 0) > 0
+                or (_fallback_entry.get("max_tokens") or 0) > 0
+                or (_fallback_entry.get("input_cost_per_token") or 0) > 0
+            )
+            if _fallback_resolves:
+                verbose_router_logger.debug(
+                    "Azure deployment '%s' has no base_model set; using '%s' from the model cost map for max tokens, cost tracking, etc.",
+                    _model,
+                    _azure_fallback_key,
+                )
+            else:
+                verbose_router_logger.error(
+                    "Could not identify azure model '%s'. Set azure 'base_model' for accurate max tokens, cost tracking, etc.- https://docs.litellm.ai/docs/proxy/cost_tracking#spend-tracking-for-azure-openai-models",
+                    _model,
+                )
         elif custom_llm_provider != "azure":
             model = _model
 

tests/test_litellm/test_router.py

@@ -8639,3 +8639,85 @@ async def test_fallback_reentry_with_a_plain_group_clears_the_stale_marker(self)
         await router.async_pre_routing_hook(model="gemini-flash", request_kwargs=request_kwargs)
 
         assert AUTO_ROUTED_REQUEST_METADATA_KEY not in request_kwargs["metadata"]
+
+
+@pytest.mark.usefixtures("local_model_cost_map")
+class TestAzureBaseModelFallbackLogging:
+    """When an azure deployment has no base_model but its model name is a known
+    azure key in the cost map, get_router_model_info resolves it via the
+    fallback, so it must not log the per-request 'Could not identify azure
+    model' ERROR. The ERROR must remain for genuinely unmappable deployment
+    names. Issue #33172."""
+
+    def _router_with_azure_deployment(self, deployment_model: str):
+        return litellm.Router(
+            model_list=[
+                {
+                    "model_name": "my-group",
+                    "litellm_params": {
+                        "model": deployment_model,
+                        "api_key": "fake-key",
+                        "api_base": "https://fake.openai.azure.com",
+                    },
+                    "model_info": {"id": "azure-base-model-test-id"},
+                }
+            ]
+        )
+
+    def test_map_known_deployment_name_resolves_without_error_log(self):
+        router = self._router_with_azure_deployment("azure/gpt-4o")
+
+        with patch(
+            "litellm.router.verbose_router_logger.error"
+        ) as mock_error:
+            model_info = router.get_router_model_info(
+                deployment=None, received_model_name="my-group", id="azure-base-model-test-id"
+            )
+
+        assert not any(
+            "Could not identify azure model" in str(call)
+            for call in mock_error.call_args_list
+        ), f"unexpected error log: {mock_error.call_args_list}"
+        # the fallback resolution must actually surface the map values
+        assert model_info["max_input_tokens"] == litellm.model_cost["azure/gpt-4o"]["max_input_tokens"]
+        assert model_info["input_cost_per_token"] == litellm.model_cost["azure/gpt-4o"]["input_cost_per_token"]
+
+    def test_unmappable_deployment_name_still_logs_error(self):
+        router = self._router_with_azure_deployment("azure/my-custom-deployment-name")
+
+        with patch(
+            "litellm.router.verbose_router_logger.error"
+        ) as mock_error:
+            model_info = router.get_router_model_info(
+                deployment=None, received_model_name="my-group", id="azure-base-model-test-id"
+            )
+
+        assert any(
+            "Could not identify azure model" in str(call)
+            for call in mock_error.call_args_list
+        ), "expected the error log for an unmappable azure deployment name"
+        # unmappable names resolve to a zeroed stub — unchanged behavior
+        assert model_info.get("max_input_tokens") is None
+
+    def test_explicit_base_model_still_wins(self):
+        router = litellm.Router(
+            model_list=[
+                {
+                    "model_name": "my-group",
+                    "litellm_params": {
+                        "model": "azure/some-deployment",
+                        "api_key": "fake-key",
+                        "api_base": "https://fake.openai.azure.com",
+                    },
+                    "model_info": {
+                        "id": "azure-base-model-test-id",
+                        "base_model": "azure/gpt-4o-mini",
+                    },
+                }
+            ]
+        )
+
+        model_info = router.get_router_model_info(
+            deployment=None, received_model_name="my-group", id="azure-base-model-test-id"
+        )
+        assert model_info["max_input_tokens"] == litellm.model_cost["azure/gpt-4o-mini"]["max_input_tokens"]