diff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py
index 7e9f53b1816d1..54aa5a04e0d28 100644
--- a/vllm/entrypoints/openai/protocol.py
+++ b/vllm/entrypoints/openai/protocol.py
@@ -614,6 +614,7 @@ class EmbeddingRequest(OpenAIBaseModel):
     encoding_format: Literal["float", "base64"] = "float"
     dimensions: Optional[int] = None
     user: Optional[str] = None
+    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
 
     # doc: begin-embedding-pooling-params
     additional_data: Optional[Any] = None
diff --git a/vllm/entrypoints/openai/serving_embedding.py b/vllm/entrypoints/openai/serving_embedding.py
index 12ec6be03cd62..0cc740e27efed 100644
--- a/vllm/entrypoints/openai/serving_embedding.py
+++ b/vllm/entrypoints/openai/serving_embedding.py
@@ -110,6 +110,13 @@ async def create_embedding(
         request_id = f"embd-{random_uuid()}"
         created_time = int(time.monotonic())
 
+        truncate_prompt_tokens = None
+
+        if request.truncate_prompt_tokens is not None:
+            if request.truncate_prompt_tokens < self.max_model_len:
+                truncate_prompt_tokens = request.truncate_prompt_tokens
+            else:
+                truncate_prompt_tokens = self.max_model_len
         # Schedule the request and get the result generator.
         generators: List[AsyncGenerator[EmbeddingRequestOutput, None]] = []
         try:
@@ -128,6 +135,7 @@ async def create_embedding(
                     request,
                     tokenizer,
                     request.input,
+                    truncate_prompt_tokens
                 ))
 
             for i, prompt_inputs in enumerate(prompts):