Vision

Multimodal (vision) models accept images alongside text and respond with text — for describing, analyzing, or answering questions about an image. Vision goes through the same /v1/chat/completions endpoint as text chat; you just send image content parts instead of a plain string.

Sending an image

Set a message’s content to an array of parts. Use {"type": "text", ...} for text and {"type": "image_url", "image_url": {"url": "..."}} for an image. The url can be a public https URL or a data:image/...;base64,... data URI.

resp = client.chat.completions.create(
    model="Qwen/Qwen2.5-VL-72B-Instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {"url": "https://example.com/photo.jpg"},
                },
            ],
        }
    ],
)

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

import base64

with open("photo.jpg", "rb") as f:
    b64 = base64.b64encode(f.read()).decode()

resp = client.chat.completions.create(
    model="Qwen/Qwen2.5-VL-72B-Instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe this image."},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{b64}"},
                },
            ],
        }
    ],
)

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

const resp = await client.chat.completions.create({
  model: "Qwen/Qwen2.5-VL-72B-Instruct",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "What's in this image?" },
        {
          type: "image_url",
          image_url: { url: "https://example.com/photo.jpg" },
        },
      ],
    },
  ],
});

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

curl https://gateway.errorbar.ai/v1/chat/completions \
  -H "Authorization: Bearer $ERRORBAR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-VL-72B-Instruct",
    "messages": [{
      "role": "user",
      "content": [
        { "type": "text", "text": "What'\''s in this image?" },
        { "type": "image_url", "image_url": { "url": "https://example.com/photo.jpg" } }
      ]
    }]
  }'

Use an https URL when the image is already hosted somewhere the model can reach; use a base64 data URI for local files or images you don’t want to expose publicly. You can include multiple image_url parts in one message to ask about several images at once.

Choosing a model

Only vision-capable models accept images. Which models support vision is discoverable via GET /v1/models, for example Qwen/Qwen2.5-VL-72B-Instruct. Sending an image to a text-only model has no effect on the image content.

The response

The response is a standard chat completion: text content plus a usage object:

{
  "id": "chatcmpl-...",
  "object": "chat.completion",
  "model": "Qwen/Qwen2.5-VL-72B-Instruct",
  "choices": [
    {
      "index": 0,
      "message": { "role": "assistant", "content": "A cat sitting on a windowsill." },
      "finish_reason": "stop"
    }
  ],
  "usage": { "prompt_tokens": 264, "completion_tokens": 8, "total_tokens": 272 }
}

Billing

Vision requests are billed on prompt and completion tokens like any chat request; the image contributes to the prompt_tokens count. Because everything runs through /v1/chat/completions, vision requests also support streaming, tool calling, and structured output.

Get started

Capture

Observe

Grade & Judge

Evaluate

Improve

Run at scale

Platform

Sending an image

Choosing a model

The response

Billing

​Sending an image

​Choosing a model

​The response

​Billing

Sending an image

Choosing a model

The response

Billing