vllm/docs/source/features/reasoning_outputs.md

(reasoning-outputs)=

# Reasoning Outputs

vLLM offers support for reasoning models like [DeepSeek R1](https://huggingface.co/deepseek-ai/DeepSeek-R1), which are designed to generate outputs containing both reasoning steps and final conclusions.

Reasoning models return a additional `reasoning_content` field in their outputs, which contains the reasoning steps that led to the final conclusion. This field is not present in the outputs of other models.

## Supported Models

vLLM currently supports the following reasoning models:

- [DeepSeek R1 series](https://huggingface.co/collections/deepseek-ai/deepseek-r1-678e1e131c0169c0bc89728d) (`deepseek_r1`, which looks for `<think> ... </think>`)

## Quickstart

To use reasoning models, you need to specify the `--enable-reasoning` and `--reasoning-parser` flags when making a request to the chat completion endpoint. The `--reasoning-parser` flag specifies the reasoning parser to use for extracting reasoning content from the model output.

```bash
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B \
    --enable-reasoning --reasoning-parser deepseek_r1
```

Next, make a request to the model that should return the reasoning content in the response.

```python
from openai import OpenAI

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id

# Round 1
messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
response = client.chat.completions.create(model=model, messages=messages)

reasoning_content = response.choices[0].message.reasoning_content
content = response.choices[0].message.content

print("reasoning_content:", reasoning_content)
print("content:", content)
```

The `reasoning_content` field contains the reasoning steps that led to the final conclusion, while the `content` field contains the final conclusion.

## Streaming chat completions

Streaming chat completions are also supported for reasoning models. The `reasoning_content` field is available in the `delta` field in [chat completion response chunks](https://platform.openai.com/docs/api-reference/chat/streaming).

```json
{
    "id": "chatcmpl-123",
    "object": "chat.completion.chunk",
    "created": 1694268190,
    "model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
    "system_fingerprint": "fp_44709d6fcb",
    "choices": [
        {
            "index": 0,
            "delta": {
                "role": "assistant",
                "reasoning_content": "is",
            },
            "logprobs": null,
            "finish_reason": null
        }
    ]
}
```

Please note that it is not compatible with the OpenAI Python client library. You can use the `requests` library to make streaming requests.

## How to support a new reasoning model

You can add a new `ReasoningParser` similar to `vllm/entrypoints/openai/reasoning_parsers/deepseek_r1_reasoning_parser.py`.

```python
# import the required packages

from vllm.entrypoints.openai.reasoning_parsers.abs_reasoning_parsers import (
    ReasoningParser, ReasoningParserManager)
from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                              DeltaMessage)

# define a reasoning parser and register it to vllm
# the name list in register_module can be used
# in --reasoning-parser.
@ReasoningParserManager.register_module(["example"])
class ExampleParser(ReasoningParser):
    def __init__(self, tokenizer: AnyTokenizer):
        super().__init__(tokenizer)

    def extract_reasoning_content_streaming(
        self,
        previous_text: str,
        current_text: str,
        delta_text: str,
        previous_token_ids: Sequence[int],
        current_token_ids: Sequence[int],
        delta_token_ids: Sequence[int],
    ) -> Union[DeltaMessage, None]:
        """
        Instance method that should be implemented for extracting reasoning
        from an incomplete response; for use when handling reasoning calls and
        streaming. Has to be an instance method because  it requires state -
        the current tokens/diffs, but also the information about what has
        previously been parsed and extracted (see constructor)
        """

    def extract_reasoning_content(
            self, model_output: str, request: ChatCompletionRequest
    ) -> Tuple[Optional[str], Optional[str]]:
        """
        Extract reasoning content from a complete model-generated string.

        Used for non-streaming responses where we have the entire model response
        available before sending to the client.

        Parameters:
        model_output: str
            The model-generated string to extract reasoning content from.

        request: ChatCompletionRequest
            The request object that was used to generate the model_output.

        Returns:
        Tuple[Optional[str], Optional[str]]
            A tuple containing the reasoning content and the content.
        """
```

After defining the reasoning parser, you can use it by specifying the `--reasoning-parser` flag when making a request to the chat completion endpoint.

```bash
vllm serve <model_tag> \
    --enable-reasoning --reasoning-parser example
```

## Limitations

- The reasoning content is only available for online serving's chat completion endpoint (`/v1/chat/completions`).
- It is not compatible with the [`structured_outputs`](#structured_outputs) and [`tool_calling`](#tool_calling) features.
- The reasoning content is not available for all models. Check the model's documentation to see if it supports reasoning.