diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index ff0c1275f9d..dbffceb16ea 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -316,7 +316,7 @@ def _is_positive_finite_number(value: Any) -> bool:
     )
 
 
-def _get_metadata_model_infos(
+def _get_metadata_model_infos_for_cost_fallback(
     litellm_logging_obj: Optional[LitellmLoggingObject],
 ) -> List[Mapping[str, Any]]:
     litellm_params = getattr(litellm_logging_obj, "litellm_params", None)
@@ -339,15 +339,6 @@ def _get_max_input_tokens_for_cost_fallback(
     custom_llm_provider: Optional[str],
     litellm_logging_obj: Optional[LitellmLoggingObject],
 ) -> Optional[Union[int, float]]:
-    metadata_model_infos = _get_metadata_model_infos(
-        litellm_logging_obj=litellm_logging_obj
-    )
-    for model_info in metadata_model_infos:
-        for token_limit_key in ("max_input_tokens", "max_tokens"):
-            token_limit = model_info.get(token_limit_key)
-            if _is_positive_finite_number(token_limit):
-                return cast(Union[int, float], token_limit)
-
     if model is None:
         return None
 
@@ -369,6 +360,18 @@ def _get_max_input_tokens_for_cost_fallback(
             token_limit = model_info.get(token_limit_key)
             if _is_positive_finite_number(token_limit):
                 return cast(Union[int, float], token_limit)
+
+    # Request metadata can be caller-controlled on proxy paths. Prefer the
+    # registered model info above; use metadata only as a final compatibility
+    # fallback for router/deployment paths that have not registered the model.
+    metadata_model_infos = _get_metadata_model_infos_for_cost_fallback(
+        litellm_logging_obj=litellm_logging_obj
+    )
+    for model_info in metadata_model_infos:
+        for token_limit_key in ("max_input_tokens", "max_tokens"):
+            token_limit = model_info.get(token_limit_key)
+            if _is_positive_finite_number(token_limit):
+                return cast(Union[int, float], token_limit)
     return None
 
 
diff --git a/tests/local_testing/test_completion_cost.py b/tests/local_testing/test_completion_cost.py
index cadafc34035..3ca4ad3461c 100644
--- a/tests/local_testing/test_completion_cost.py
+++ b/tests/local_testing/test_completion_cost.py
@@ -1031,6 +1031,68 @@ def test_completion_cost_uses_conservative_video_fallback_without_usage():
     assert cost == pytest.approx(max_input_tokens * input_cost_per_token)
 
 
+@pytest.mark.parametrize("metadata_key", ["metadata", "litellm_metadata"])
+def test_completion_cost_ignores_client_metadata_for_video_fallback_limit(
+    metadata_key,
+):
+    model = "openai/test-video-untrusted-metadata-fallback"
+    input_cost_per_token = 0.25
+    max_input_tokens = 32
+    litellm.register_model(
+        model_cost={
+            model: {
+                "input_cost_per_token": input_cost_per_token,
+                "output_cost_per_token": 0.0,
+                "max_tokens": max_input_tokens,
+                "max_input_tokens": max_input_tokens,
+                "max_output_tokens": 4,
+                "litellm_provider": "openai",
+                "mode": "chat",
+            }
+        }
+    )
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "video_url",
+                    "video_url": {
+                        "url": "https://example.com/video.mp4",
+                        "video_metadata": {
+                            "duration_seconds": 0,
+                            "fps": 0,
+                            "has_audio": False,
+                        },
+                    },
+                }
+            ],
+        }
+    ]
+    logging_obj = MagicMock()
+    logging_obj.litellm_params = {
+        metadata_key: {
+            "model_info": {
+                "max_input_tokens": 1,
+                "max_tokens": 1,
+            }
+        }
+    }
+
+    try:
+        cost = completion_cost(
+            completion_response={"model": model, "usage": {}},
+            model=model,
+            messages=messages,
+            custom_llm_provider="openai",
+            litellm_logging_obj=logging_obj,
+        )
+    finally:
+        litellm.model_cost.pop(model, None)
+
+    assert cost == pytest.approx(max_input_tokens * input_cost_per_token)
+
+
 def test_completion_cost_uses_provider_video_usage_when_present():
     model = "openai/test-video-provider-usage"
     input_cost_per_token = 0.25