Fix spend logs storing router model instead of actual selected model

get_logging_payload re-derived the model from request kwargs via
reconstruct_model_name, discarding standard_logging_payload["model"]
which already carries the Azure Model Router override (the model Azure
actually selected). Prefer the standard logging payload model when set
and fall back to kwargs reconstruction otherwise; for non-router calls
the two are built from the same inputs so behavior is unchanged.

Fixes #27942.

litellm/proxy/spend_tracking/spend_tracking_utils.py

@@ -411,7 +411,13 @@ def get_logging_payload(  # noqa: PLR0915
     agent_id: Optional[str] = kwargs.get("agent_id") or metadata.get("agent_id")
     custom_llm_provider = kwargs.get("custom_llm_provider")
     raw_model = cast(str, kwargs.get("model") or "")
-    model_name = reconstruct_model_name(raw_model, custom_llm_provider, metadata or {})
+    # prefer the standard logging payload's model; it carries response-level
+    # overrides (e.g. the model Azure Model Router actually selected)
+    model_name = (
+        standard_logging_payload.get("model")
+        if standard_logging_payload is not None
+        else None
+    ) or reconstruct_model_name(raw_model, custom_llm_provider, metadata or {})
 
     try:
         payload: SpendLogsPayload = SpendLogsPayload(

tests/test_litellm/proxy/spend_tracking/test_spend_tracking_utils.py

@@ -2073,3 +2073,52 @@ def test_sanitize_error_information_redacts_pydantic_assignment_form(
     assert sanitized is not None
     assert "leaked-via-pydantic-msg" not in sanitized["error_message"]
     assert REDACTED_BY_LITELM_STRING in sanitized["error_message"]
+
+
+def _model_router_spend_log_kwargs(slp_model) -> dict:
+    standard_logging_payload = cast(
+        StandardLoggingPayload,
+        {
+            "model": slp_model,
+            "metadata": {},
+            "model_map_information": StandardLoggingModelInformation(
+                model_map_key="azure_ai/model_router", model_map_value=None
+            ),
+        },
+    )
+    return {
+        "model": "azure_ai/model_router/model-router",
+        "litellm_params": {"metadata": {"user_api_key": "sk-test-key"}},
+        "standard_logging_object": standard_logging_payload,
+    }
+
+
+@patch("litellm.proxy.proxy_server.master_key", None)
+@patch("litellm.proxy.proxy_server.general_settings", {})
+def test_get_logging_payload_uses_standard_logging_payload_model():
+    """
+    Azure Model Router regression: the standard logging payload model (the
+    model Azure actually selected) must win over the router model
+    reconstructed from request kwargs.
+    """
+    payload = get_logging_payload(
+        kwargs=_model_router_spend_log_kwargs(slp_model="azure_ai/gpt-5-mini"),
+        response_obj={},
+        start_time=datetime.datetime.now(timezone.utc),
+        end_time=datetime.datetime.now(timezone.utc),
+    )
+
+    assert payload["model"] == "azure_ai/gpt-5-mini"
+
+
+@patch("litellm.proxy.proxy_server.master_key", None)
+@patch("litellm.proxy.proxy_server.general_settings", {})
+def test_get_logging_payload_falls_back_to_kwargs_model_when_slp_model_missing():
+    payload = get_logging_payload(
+        kwargs=_model_router_spend_log_kwargs(slp_model=None),
+        response_obj={},
+        start_time=datetime.datetime.now(timezone.utc),
+        end_time=datetime.datetime.now(timezone.utc),
+    )
+
+    assert payload["model"] == "azure_ai/model_router/model-router"