vision.mdx

---
title: Vision
---

Vision models accept images alongside text so the model can describe, classify, and answer questions about what it sees.

## Quick start

```shell
ollama run gemma3 ./image.png whats in this image?
```


## Usage with Ollama's API
Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the REST API expects base64-encoded image data.


<Tabs>
  <Tab title="cURL">
    ```shell
    # 1. Download a sample image
    curl -L -o test.jpg "https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg"

    # 2. Encode the image
    IMG=$(base64 < test.jpg | tr -d '\n')

    # 3. Send it to Ollama
    curl -X POST http://localhost:11434/api/chat \
    -H "Content-Type: application/json" \
    -d '{
        "model": "gemma3",
        "messages": [{
        "role": "user",
        "content": "What is in this image?",
        "images": ["'"$IMG"'"]
        }],
        "stream": false
    }'
    "
    ```
  </Tab>
  <Tab title="Python">
    ```python
    from ollama import chat
    # from pathlib import Path

    # Pass in the path to the image
    path = input('Please enter the path to the image: ')

    # You can also pass in base64 encoded image data
    # img = base64.b64encode(Path(path).read_bytes()).decode()
    # or the raw bytes
    # img = Path(path).read_bytes()

    response = chat(
      model='gemma3',
      messages=[
        {
          'role': 'user',
          'content': 'What is in this image? Be concise.',
          'images': [path],
        }
      ],
    )

    print(response.message.content)
    ```
  </Tab>
  <Tab title="JavaScript">
    ```javascript
    import ollama from 'ollama'

    const imagePath = '/absolute/path/to/image.jpg'
    const response = await ollama.chat({
      model: 'gemma3',
      messages: [
        { role: 'user', content: 'What is in this image?', images: [imagePath] }
      ],
      stream: false,
    })

    console.log(response.message.content)
    ```
  </Tab>
</Tabs>