Serve reasoning models

Your Qwen3 server returns a long block of thinking where you expected a short answer. When you finish this section, you can separate the thinking from the answer, turn the thinking off, and limit its length.

We did not measure the speed of reasoning models for this manual. We checked the fields and switches on this page on an L4 with Qwen3-8B-FP8. That covers the reasoning field, enable_thinking per request and per server, the thinking budget, and the stream order. The rest comes from the vLLM documentation and the source at 0.31.0.

Know what the model writes

Reasoning model: a model that writes its reasoning steps first, between markers such as <think> and </think>, and then the final answer. Qwen3, DeepSeek-R1, and QwQ are examples. Qwen3 thinks by default.

Without a reasoning parser, vLLM returns the whole output in content. The answer then starts with the thinking. The thinking also counts against max_tokens, so a short limit can end the request before the answer starts.

Turn on the parser

Start the server with the parser for your model family. For Qwen3, the parser is qwen3:

vllm serve Qwen/Qwen3-8B --reasoning-parser qwen3 2>&1 | tee server.log

The default is no parser. vLLM does not pick one from the model name. The vLLM documentation lists the parser name for each supported family, such as deepseek_r1 for DeepSeek-R1 and QwQ.

With the parser, a chat response has two text fields:

In a stream, each chunk carries the new text in delta.reasoning or in delta.content. Older releases called the field reasoning_content. At 0.31.0 the response field is reasoning, so update a client that reads the old name.

Turn the thinking off or limit it

You have three controls:

• For one request. Send "chat_template_kwargs": {"enable_thinking": false} in the request body. With the OpenAI client, put it in extra_body.
• For the whole server. Start the server with --default-chat-template-kwargs '{"enable_thinking": false}'. A request can still turn the thinking on again with its own chat_template_kwargs.
• A budget for the thinking. Send thinking_token_budget in the request. When the thinking reaches that many tokens, vLLM makes the model close the thinking and start the answer. The parser must be on.

The name enable_thinking comes from the Qwen3 chat template. Other families use other names, so read the chat template of your model.

Know what changes for JSON and tools

With a reasoning parser on, structured output applies to the answer only, after the thinking ends. The thinking itself is free text. The Qwen3 parser also works with tool calls, so a request can return reasoning and tool_calls together. For tool calls, also start the server with --enable-auto-tool-choice and a tool call parser, such as --tool-call-parser hermes for Qwen3. The JSON page shows how to ask for a schema.

Give a thinking request a large max_tokens. If the limit ends the request inside the thinking, content is empty and finish_reason is length. A response that a limit cut is not valid JSON.

Check the setup

Send one request with the thinking on and one with it off:

curl -s http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" \
  -d '{"model": "Qwen/Qwen3-8B", "messages": [{"role": "user", "content": "Is 91 a prime number?"}], "max_tokens": 2048}'

The first reply has text in both reasoning and content. Add "chat_template_kwargs": {"enable_thinking": false} to the body and send it again. The second reply has an empty or missing reasoning field and a short content.