fix(vertex): bill routed generateContent at the deployment's custom pricing

The Vertex pass-through logging handler priced generateContent calls,
including every streamed /v1beta/models/{model}:streamGenerateContent
call, with completion_cost(model, custom_llm_provider) alone, so it read
the cost map's shared entry for the backend model, from which the router
strips custom pricing on purpose. A deployment with its own
input_cost_per_token and output_cost_per_token was billed at the bundled
price. Pass custom_pricing and router_model_id, as the Anthropic handler
already does, so the deployment's registered price wins.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py

@@ -12,6 +12,7 @@ import litellm
 from litellm._logging import verbose_proxy_logger
 from litellm.constants import VERTEX_BATCH_PREDICTION_JOBS_ROUTE
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.litellm_core_utils.litellm_logging import use_custom_pricing_for_model
 from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import (
     InteractionsUsageObjectTransformation,
 )
@@ -769,11 +770,16 @@ class VertexPassthroughLoggingHandler:
 
         """
 
+        # A routed call carries the deployment it was routed to; price it at
+        # that deployment's custom pricing, as the Anthropic handler does,
+        # rather than at the cost map's shared entry for the backend model.
         response_cost: Final = litellm.completion_cost(
             completion_response=litellm_model_response,
             model=model,
             custom_llm_provider=custom_llm_provider,
             vertex_location=vertex_location,
+            custom_pricing=use_custom_pricing_for_model(litellm_params=getattr(logging_obj, "litellm_params", None)),
+            router_model_id=logging_obj.get_router_model_id(),
         )
 
         kwargs["response_cost"] = response_cost

tests/unit/proxy/pass_through_endpoints/llm_provider_handlers/test_vertex_passthrough_logging_handler.py

@@ -127,3 +127,50 @@ def test_build_complete_streaming_response_assembles_stream_generate_content_sse
     assert response.choices[0].message.content == "Hello there!"
     assert response.choices[0].finish_reason == "stop"
     assert (response.usage.prompt_tokens, response.usage.completion_tokens, response.usage.total_tokens) == (10, 8, 18)
+
+
+def test_generate_content_cost_uses_the_deployments_custom_pricing(monkeypatch: pytest.MonkeyPatch):
+    """A routed generateContent call (streamed calls are logged through this
+    path) is billed at the price set on the deployment it was routed to, not
+    the cost map's shared price for the backend model."""
+    import litellm
+    from litellm.types.utils import Usage
+
+    deployment_id: Final = "vertex-gemini-with-custom-price"
+    custom_price: Final = {"input_cost_per_token": 1.5e-6, "output_cost_per_token": 7.5e-6}
+    monkeypatch.setitem(
+        litellm.model_cost,
+        deployment_id,
+        {**custom_price, "litellm_provider": "vertex_ai-language-models", "mode": "chat"},
+    )
+    logging_obj: Final = Logging(
+        model="gemini-2.5-flash",
+        messages=[{"role": "user", "content": "hi"}],
+        stream=True,
+        call_type="pass_through_endpoint",
+        start_time=datetime(2026, 1, 1),
+        litellm_call_id="call-1",
+        function_id="fn-1",
+    )
+    logging_obj.update_environment_variables(
+        litellm_params={"metadata": {"model_info": {"id": deployment_id, **custom_price}}},
+        optional_params={},
+        model="gemini-2.5-flash",
+    )
+    response: Final = ModelResponse(
+        model="gemini-2.5-flash",
+        usage=Usage(prompt_tokens=1_000_000, completion_tokens=1_000_000, total_tokens=2_000_000),
+    )
+
+    kwargs: Final = VertexPassthroughLoggingHandler._create_vertex_response_logging_payload_for_generate_content(
+        litellm_model_response=response,
+        model="gemini-2.5-flash",
+        kwargs={},
+        start_time=datetime(2026, 1, 1),
+        end_time=datetime(2026, 1, 1),
+        logging_obj=logging_obj,
+        custom_llm_provider="vertex_ai",
+        vertex_location=None,
+    )
+
+    assert kwargs["response_cost"] == pytest.approx(1.5 + 7.5)