> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ollama.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Thinking

Thinking-capable models emit a `thinking` field that separates their reasoning trace from the final answer.

Use this capability to audit model steps, animate the model *thinking* in a UI, or hide the trace entirely when you only need the final response.

See the [full list of thinking models](https://ollama.com/search?c=thinking).

## Discover a model's thinking controls

Thinking controls vary by model. Use `/api/show` to discover the values a model supports and the value Ollama uses by default:

```shell theme={"system"}
curl http://localhost:11434/api/show -d '{
  "model": "gpt-oss"
}'
```

The response includes a top-level `thinking` object:

```json theme={"system"}
{
  "thinking": {
    "values": ["low", "medium", "high"],
    "default": "medium"
  }
}
```

* `values` can contain booleans (`true` or `false`) for on/off controls. It can also contain model-defined strings for named levels.
* `default` is used when `think` is not set.
* `values: [false]` means the model does not support thinking.
* If `thinking` is omitted, the model has no thinking metadata. The model might conduct thinking based on its existing behavior.

## Enable thinking in API calls

Set the `think` field on a chat or generate request:

* `true`: request thinking output.
* `false`: request no thinking output, if the model permits it.
* `null`: use the model default.
* A string: select a supported level from `thinking.values`. Use the exact value from `/api/show`. Numbers are not supported.

If the model resolves named levels from `/api/show` metadata, Ollama applies supported names exactly. Unsupported names use the model default.

The reasoning output and answer use separate fields. Chat returns `message.thinking` and `message.content`. Generate returns `thinking` and `response`.

<Tabs>
  <Tab title="cURL">
    ```shell theme={"system"}
    curl http://localhost:11434/api/chat -d '{
      "model": "qwen3",
      "messages": [{
        "role": "user",
        "content": "How many letter r are in strawberry?"
      }],
      "think": true,
      "stream": false
    }'
    ```
  </Tab>

  <Tab title="Python">
    ```python theme={"system"}
    from ollama import chat

    response = chat(
      model='qwen3',
      messages=[{'role': 'user', 'content': 'How many letter r are in strawberry?'}],
      think=True,
      stream=False,
    )

    print('Thinking:\n', response.message.thinking)
    print('Answer:\n', response.message.content)
    ```
  </Tab>

  <Tab title="JavaScript">
    ```javascript theme={"system"}
    import ollama from 'ollama'

    const response = await ollama.chat({
      model: 'deepseek-r1',
      messages: [{ role: 'user', content: 'How many letter r are in strawberry?' }],
      think: true,
      stream: false,
    })

    console.log('Thinking:\n', response.message.thinking)
    console.log('Answer:\n', response.message.content)
    ```
  </Tab>
</Tabs>

## Stream the reasoning trace

Thinking streams interleave reasoning tokens before answer tokens. Detect the first `thinking` chunk to render a "thinking" section, then switch to the final reply once `message.content` arrives.

<Tabs>
  <Tab title="Python">
    ```python theme={"system"}
    from ollama import chat

    stream = chat(
      model='qwen3',
      messages=[{'role': 'user', 'content': 'What is 17 × 23?'}],
      think=True,
      stream=True,
    )

    in_thinking = False

    for chunk in stream:
      if chunk.message.thinking and not in_thinking:
        in_thinking = True
        print('Thinking:\n', end='')

      if chunk.message.thinking:
        print(chunk.message.thinking, end='')
      elif chunk.message.content:
        if in_thinking:
          print('\n\nAnswer:\n', end='')
          in_thinking = False
        print(chunk.message.content, end='')

    ```
  </Tab>

  <Tab title="JavaScript">
    ```javascript theme={"system"}
    import ollama from 'ollama'

    async function main() {
      const stream = await ollama.chat({
        model: 'qwen3',
        messages: [{ role: 'user', content: 'What is 17 × 23?' }],
        think: true,
        stream: true,
      })

      let inThinking = false

      for await (const chunk of stream) {
        if (chunk.message.thinking && !inThinking) {
          inThinking = true
          process.stdout.write('Thinking:\n')
        }

        if (chunk.message.thinking) {
          process.stdout.write(chunk.message.thinking)
        } else if (chunk.message.content) {
          if (inThinking) {
            process.stdout.write('\n\nAnswer:\n')
            inThinking = false
          }
          process.stdout.write(chunk.message.content)
        }
      }
    }

    main()
    ```
  </Tab>
</Tabs>