`;
});
clearInterval(interval);
}
attempts++;
if (attempts >= maxAttempts) {
clearInterval(interval);
}
}, 200);
return ;
}
};
## Overview
Vision Language Models (VLMs) are a class of multimodal large models that support both image and text inputs, with the ability to understand image content and process cross-modal information. These models can output high-quality responses based on combined image and text information, and are widely used in scenarios such as image recognition, content understanding, and intelligent Q\&A.
### Typical Use Cases
* **Image Content Recognition & Description**: Automatically recognize objects, colors, scenes, and spatial relationships in images, and generate natural language descriptions.
* **Image-Text Comprehension**: Combine image and text inputs to enable context-aware multi-turn conversations and complex task responses.
* **Visual-Aided Q\&A**: Can serve as a supplement to OCR tools, recognizing text embedded in images and completing Q\&A tasks.
* **Future Applications**: Suitable for intelligent visual assistants, robotic perception, augmented reality, and other interactive scenarios.
## API Usage
Calling Vision Language Models requires the `/chat/completions` endpoint, which supports mixed image-text inputs.
### Image Processing Parameters
Use the `detail` field to set image processing precision, with the following options:
* `high`: High resolution, preserves more details, suitable for fine-grained tasks.
* `low`: Low resolution, faster processing, suitable for real-time responses.
* `auto`: The system automatically selects the appropriate mode.
### Message Format Examples
#### URL Image Format
```json theme={null}
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://example.com/image.png",
"detail": "high"
}
},
{
"type": "text",
"text": "Please describe the scene in the image."
}
]
}
```
#### Base64 Image Format
```json theme={null}
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "data:image/jpeg;base64,{base64_image}",
"detail": "low"
}
},
{
"type": "text",
"text": "What text content is in the image?"
}
]
}
```
### Base64 Image Encoding Example Code (Python)
```python theme={null}
import base64
from PIL import Image
import io
def image_to_base64(image_path):
with Image.open(image_path) as img:
buffered = io.BytesIO()
img.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode('utf-8')
base64_image = image_to_base64("path/to/your/image.jpg")
```
## Multi-Image Mode
Supports sending multiple images along with text as input. It is recommended to use no more than two images for optimal performance and comprehension.
```json theme={null}
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://example.com/image1.png"
}
},
{
"type": "image_url",
"image_url": {
"url": "data:image/jpeg;base64,{base64_image}"
}
},
{
"type": "text",
"text": "Compare the common features of these two images."
}
]
}
```
## Supported Models
The following are the currently supported Vision Language Models (VLMs):
## Pricing
Image inputs for Vision Language Models are converted to tokens and calculated together with text for billing:
* Token estimation rules for images vary slightly between models;
* Detailed pricing can be found on each model's detail page.
## API Call Example Code
### Single Image Description
```python theme={null}
from openai import OpenAI
client = OpenAI(api_key="YOUR_KEY", base_url="https://api.myrouter.ai/openai")
response = client.chat.completions.create(
model="qwen/qwen2.5-vl-72b-instruct",
messages=[
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "https://example.com/cityscape.jpg"}},
{"type": "text", "text": "Describe the main buildings in the image."}
]
}
],
stream=True
)
for chunk in response:
print(chunk.choices[0].delta.content or "", end="", flush=True)
```
### Multi-Image Comparative Analysis
```python theme={null}
response = client.chat.completions.create(
model="qwen/qwen2.5-vl-72b-instruct",
messages=[
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "https://example.com/product1.jpg"}},
{"type": "image_url", "image_url": {"url": "https://example.com/product2.jpg"}},
{"type": "text", "text": "Please compare the main differences between these two products."}
]
}
],
stream=True
)
for chunk in response:
print(chunk.choices[0].delta.content or "", end="", flush=True)
```
## FAQ & Notes
* Image resolution and clarity affect model recognition accuracy; clear image sources are recommended.
* Base64 encoding produces large payloads; it is recommended to keep images under 1MB.
* If you encounter issues, please refer to the platform developer documentation or submit a support ticket.