# Image Processing

### Overview

FastRouter lets you include images in chat completions using an OpenAI-compatible message format. This enables vision use cases like image captioning, visual Q\&A, object recognition, and multimodal chat across supported providers and models.

***

### Supported Image Types

* **Images:** URLs or base64-encoded data URLs
  * Common formats: **jpg, jpeg, png, webp** (and others depending on the model/provider)

***

### Supported Models

Use models that support **Image** as an input modality in the FastRouter model catalog:\
<https://fastrouter.ai/models?input_modalities=image>

Examples include multimodal/vision-capable models such as `x-ai/grok-4` (availability varies).

***

### Sending Image Inputs

Images are sent inside `messages[].content[]` as entries of type `"image_url"`, either pointing to a public URL or a base64-encoded data URL.

#### Example: Sending an Image via URL (Python)

```python
import requests

url = "https://api.fastrouter.ai/api/v1/chat/completions"
headers = {
  "Authorization": "Bearer API-KEY",
  "Content-Type": "application/json"
}

messages = [
  {
    "role": "user",
    "content": [
      {"type": "text", "text": "Describe this image and identify any landmarks."},
      {
        "type": "image_url",
        "image_url": {
          "url": "https://upload.wikimedia.org/wikipedia/commons/d/da/Taj-Mahal.jpg"
        }
      }
    ]
  }
]

payload = {
  "model": "openai/gpt-4.1-nano",
  "messages": messages
}

response = requests.post(url, headers=headers, json=payload)
print(response.json())
```

#### Example: Sending a Base64-Encoded Image (Python)

Use base64 data URLs for local/private images.

```python
import requests
import base64

def encode_image_to_base64(image_path: str) -> str:
  with open(image_path, "rb") as f:
    return base64.b64encode(f.read()).decode("utf-8")

url = "https://api.fastrouter.ai/api/v1/chat/completions"
headers = {
  "Authorization": "Bearer API-KEY",
  "Content-Type": "application/json"
}

image_path = "/path/to/your/image.jpg"
base64_image = encode_image_to_base64(image_path)

# Match the MIME type to your file (e.g., image/png for .png)
data_url = f"data:image/jpeg;base64,{base64_image}"

messages = [
  {
    "role": "user",
    "content": [
      {"type": "text", "text": "What's in this image?"},
      {"type": "image_url", "image_url": {"url": data_url}}
    ]
  }
]

payload = {
  "model": "openai/gpt-4.1-nano",
  "messages": messages
}

response = requests.post(url, headers=headers, json=payload)
print(response.json())
```

***

### Tips & Best Practices

* **Put text first:** Send your prompt before the image(s) for best results.
* **Multiple images:** Add multiple `"image_url"` items within the same `content` array if your model supports it.
* **Prefer URLs for large images:** URLs avoid base64 overhead and request-size limits.
* **Use the correct MIME type:** For base64 data URLs, ensure `data:image/...` matches the actual file type.
* **Model limits vary:** Image count/size support differs by model/provider — check the model’s docs in the FastRouter catalog.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.fastrouter.ai/image-processing.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.