diff --git a/vllm/benchmarks/serve.py b/vllm/benchmarks/serve.py
index 254e4d35e5350..f5d8ea5a975a9 100644
--- a/vllm/benchmarks/serve.py
+++ b/vllm/benchmarks/serve.py
@@ -235,7 +235,9 @@ async def get_request(
 
 
 def calculate_metrics_for_embeddings(
-    outputs: list[RequestFuncOutput], dur_s: float, selected_percentiles: list[float]
+    outputs: list[RequestFuncOutput],
+    dur_s: float,
+    selected_percentiles: list[float],
 ) -> EmbedBenchmarkMetrics:
     """Calculate the metrics for the embedding requests.
 
diff --git a/vllm/entrypoints/pooling/score/protocol.py b/vllm/entrypoints/pooling/score/protocol.py
index a22219707c357..e81bda2eec3d7 100644
--- a/vllm/entrypoints/pooling/score/protocol.py
+++ b/vllm/entrypoints/pooling/score/protocol.py
@@ -120,6 +120,7 @@ class RerankResult(BaseModel):
 
 
 class RerankUsage(BaseModel):
+    prompt_tokens: int
     total_tokens: int
 
 
diff --git a/vllm/entrypoints/pooling/score/serving.py b/vllm/entrypoints/pooling/score/serving.py
index f574d8bcebb40..edbfcd03ac92c 100644
--- a/vllm/entrypoints/pooling/score/serving.py
+++ b/vllm/entrypoints/pooling/score/serving.py
@@ -502,5 +502,7 @@ class ServingScores(OpenAIServing):
             id=request_id,
             model=model_name,
             results=results,
-            usage=RerankUsage(total_tokens=num_prompt_tokens),
+            usage=RerankUsage(
+                total_tokens=num_prompt_tokens, prompt_tokens=num_prompt_tokens
+            ),
         )