# Vision — Image & Video Multimodal Analysis

Source: https://ofox.ai/docs/develop/guides/vision

OfoxAI supports visual input for multimodal models, enabling analysis of images, screenshots, documents, and video content.

## Supported Models

| Model | Images | Video | Description |
|-------|--------|-------|-------------|
| `openai/gpt-6.1-sol` | ✅ | — | High-quality image analysis |
| `openai/gpt-6-luna` | ✅ | — | Fast image analysis |
| `anthropic/claude-sonnet-5.5` | ✅ | — | Strong document and code understanding |
| `google/gemini-3.8-flash` | ✅ | — | Multimodal all-rounder |
| `google/gemini-3.1-pro-preview` | ✅ | — | Most capable multimodal reasoning |

## Image Analysis

### Sending Images via URL

**cURL**

```bash filename="Terminal"
curl https://api.ofox.ai/v1/chat/completions \
  -H "Authorization: Bearer $OFOX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-6.1-sol",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Describe what is in this image"},
        {"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}}
      ]
    }]
  }'
```

**Python**

```python filename="vision_url.py" showLineNumbers
from openai import OpenAI

client = OpenAI(
    base_url="https://api.ofox.ai/v1",
    api_key="<your OFOXAI_API_KEY>"
)

response = client.chat.completions.create(
    model="openai/gpt-6.1-sol",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe what is in this image"},
            {
                "type": "image_url",
                "image_url": {"url": "https://example.com/photo.jpg"}
            }
        ]
    }]
)

print(response.choices[0].message.content)
```

**TypeScript**

```typescript filename="vision_url.ts" showLineNumbers
const response = await client.chat.completions.create({
  model: 'openai/gpt-6.1-sol',
  messages: [{
    role: 'user',
    content: [
      { type: 'text', text: 'Describe what is in this image' },
      {
        type: 'image_url',
        image_url: { url: 'https://example.com/photo.jpg' }
      }
    ]
  }]
})
```

### Sending Images via Base64

Suitable for local files or screenshot scenarios:

```python filename="vision_base64.py" showLineNumbers
import base64

# Read local image
with open("screenshot.png", "rb") as f:
    image_data = base64.standard_b64encode(f.read()).decode("utf-8")

response = client.chat.completions.create(
    model="openai/gpt-6.1-sol",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What does this screenshot show?"},
            {
                "type": "image_url",
                "image_url": {
                    "url": f"data:image/png;base64,{image_data}"
                }
            }
        ]
    }]
)
```

## Image Detail Level

Control analysis precision with the `detail` parameter:

| Value | Description | Use Case |
|-------|-------------|----------|
| `auto` | Automatic selection (default) | General scenarios |
| `low` | Lower precision, faster | Simple classification, tag identification |
| `high` | Higher precision, more detailed | Document OCR, detailed analysis |

```python
{
    "type": "image_url",
    "image_url": {
        "url": "https://example.com/document.jpg",
        "detail": "high"  # High precision mode
    }
}
```

## Multi-Image Comparison

You can send multiple images in a single request:

```python
response = client.chat.completions.create(
    model="openai/gpt-6.1-sol",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Compare the differences between these two images"},
            {"type": "image_url", "image_url": {"url": "https://example.com/before.jpg"}},
            {"type": "image_url", "image_url": {"url": "https://example.com/after.jpg"}}
        ]
    }]
)
```

## Vision Input with Anthropic Protocol

```python
import anthropic

client = anthropic.Anthropic(
    base_url="https://api.ofox.ai/anthropic",
    api_key="<your OFOXAI_API_KEY>"
)

message = client.messages.create(
    model="anthropic/claude-sonnet-5.5",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "image",
                "source": {
                    "type": "base64",
                    "media_type": "image/jpeg",
                    "data": image_data
                }
            },
            {"type": "text", "text": "Describe this image"}
        ]
    }]
)
```

## Common Use Cases

- **Document OCR** — Extract text and tables from images
- **Code screenshot analysis** — Analyze code in screenshots and provide suggestions
- **UI review** — Analyze interface design and layout
- **Chart interpretation** — Analyze data charts and visualizations
- **Object recognition** — Identify objects and scenes in images