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Requesty router supports sending images to vision-capable models for analysis, understanding, and description through the standard chat completions endpoint.

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How It Works

Vision-capable models use the same /v1/chat/completions endpoint as text models, but accept images in the message content. You can include images using either data URLs or file URLs.

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Request Format

Requesty supports two methods for sending images to vision models:

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Method 1: Data URLs (Base64)

Include base64-encoded images directly in the request: 
curl https://router.requesty.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -d '{
    "model": "openai/gpt-4o",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What is in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAUA..."
            }
          }
        ]
      }
    ]
  }'

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Method 2: File URLs

Reference images hosted on the web: 
curl https://router.requesty.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -d '{
    "model": "anthropic/claude-3-5-sonnet-20241022",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Describe this image in detail"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://code-basics.com/rails/active_storage/representations/proxy/eyJfcmFpb.png"
            }
          }
        ]
      }
    ]
  }'
Vertex AI Gemini Models: When using file URLs with Vertex AI Gemini models, you must specify the MIME type as a query parameter:
https://example.com/image.png?mime=image/png

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Response Format

The response follows the standard chat completions format with the model’s analysis of the image: 
{
  "model": "openai/gpt-4o",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The image shows a beautiful sunset over mountain ranges. The sky is painted with vibrant shades of orange, pink, and purple, with the sun just touching the horizon. The mountains are silhouetted against the colorful sky, creating dramatic layers of depth."
      }
    }
  ]
}

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Code Examples

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Python Example

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Using File URLs

from openai import OpenAI

requesty_api_key = "YOUR_REQUESTY_API_KEY"

client = OpenAI(
    api_key=requesty_api_key,
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="anthropic/claude-3-5-sonnet-20241022",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What's in this image?"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://code-basics.com/rails/active_storage/representations/proxy/eyJfcmFpb.png"
                    }
                }
            ]
        }
    ]
)

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

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Using Base64 Data URLs

import base64
from openai import OpenAI

requesty_api_key = "YOUR_REQUESTY_API_KEY"

client = OpenAI(
    api_key=requesty_api_key,
    base_url="https://router.requesty.ai/v1",
)

# Read and encode local image
with open("path/to/image.png", "rb") as image_file:
    base64_image = base64.b64encode(image_file.read()).decode('utf-8')

response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Analyze this image and describe what you see"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/png;base64,{base64_image}"
                    }
                }
            ]
        }
    ]
)

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

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JavaScript/TypeScript Example

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Using File URLs

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'YOUR_REQUESTY_API_KEY',
  baseURL: 'https://router.requesty.ai/v1',
});

async function analyzeImage() {
  const response = await client.chat.completions.create({
    model: 'anthropic/claude-3-5-sonnet-20241022',
    messages: [
      {
        role: 'user',
        content: [
          {
            type: 'text',
            text: 'What objects can you identify in this image?'
          },
          {
            type: 'image_url',
            image_url: {
              url: 'https://code-basics.com/rails/active_storage/representations/proxy/eyJfcmFpb.png'
            }
          }
        ]
      }
    ]
  });

  console.log(response.choices[0].message.content);
}

analyzeImage();

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Using Base64 Data URLs

import OpenAI from 'openai';
import fs from 'fs';

const client = new OpenAI({
  apiKey: 'YOUR_REQUESTY_API_KEY',
  baseURL: 'https://router.requesty.ai/v1',
});

async function analyzeLocalImage() {
  // Read and encode local image
  const imageBuffer = fs.readFileSync('path/to/image.png');
  const base64Image = imageBuffer.toString('base64');

  const response = await client.chat.completions.create({
    model: 'openai/gpt-4o',
    messages: [
      {
        role: 'user',
        content: [
          {
            type: 'text',
            text: 'Describe this image in detail'
          },
          {
            type: 'image_url',
            image_url: {
              url: `data:image/png;base64,${base64Image}`
            }
          }
        ]
      }
    ]
  });

  console.log(response.choices[0].message.content);
}

analyzeLocalImage();

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Supported Models

Requesty supports vision capabilities across multiple AI providers. To see the complete list of models with vision support, navigate to the Model Library in the Requesty web application. Popular vision-capable models include:
  • OpenAI: GPT models, o3, o3-pro, and all o4 reasoning models.
  • Anthropic: Claude 4 and 4.5 models.
  • Google: Gemini 2.5 models.
  • xAI: Grok 4 models.

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Provider-Specific Notes

  • Most providers support both data URLs and file URLs
  • Google AI Studio (Gemini) only supports data URLs (base64-encoded images)
  • Vertex AI Gemini requires the MIME type to be specified as a query parameter when using file URLs: 
    https://example.com/image.png?mime=image/png
Check the Model Library in the Requesty web application for the most up-to-date list of vision-capable models and their specific capabilities.

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Limitations

  • Image size limits vary by provider and model
  • File URLs must be publicly accessible
  • Base64-encoded images increase request payload size
  • Response time may be longer for image analysis compared to text-only requests
  • Some providers have content filtering or safety restrictions for image analysis
  • For Gemini models using file URLs, MIME type must be specified as a query parameter