> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aihubmix.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Vision

> Learn how to use visual capabilities to understand images.

## Capability Overview

The Vision capability suporta the model in understanding both images and text simultaneously, allowing for analysis, description, judgment, and question-answering based on image content. Developers can send one or more images to the model in a single request, along with natural language instructions, to complete multimodal understanding tasks. Typical capabilities include:

* Image content description (objects, scenes, actions)
* Image question answering (asking questions about the image)
* Comparative analysis and synthesis of multiple images
* Joint reasoning with images + text

## Início Rápido

```python theme={null}
from openai import OpenAI

client = OpenAI(
  api_key="<AIHUBMIX_API_KEY>",
  base_url="https://aihubmix.com/v1"
)

response = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What’s in this image?"
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
            "detail": "auto"
          },
        },
      ],
    }
  ],
  max_tokens=300,
)

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

## Supported Input Formats

Images can be provided to the model in two main ways: by passing the image link or by directly including a base64-encoded image no request. Images can be included in `user`, `system`, and `assistant` messages. Atualmente, images are not supported no first `system` message.

### Image URL Input (Recommended)

Directly pass an image URL accessible a partir do public internet, suitable for online business scenarios.

```json theme={null}
{
  "type": "image_url",
  "image_url": {
    "url": "https://example.com/demo.jpg"
  }
}
```

<Tip>
  **Notes:**

  * The URL must be accessible to the model.
  * The image format should be PNG / JPEG / WEBP / non-GIF.
  * The size of a single image must not exceed 20MB.
</Tip>

### Base64 Encoded Image Input

Suitable for local files or private image scenarios.

**Process Description:**

1. Read the image file locally.
2. Convert it to a base64 string.
3. Pass it as image content no request.

```json theme={null}
{
  "type": "image_url",
  "image_url": {
    "url": "data:image/png;base64,<BASE64_DATA>"
  }
}
```

## Message Structure Example

Images are typically sent alongside text instructions to clarify the model's understanding objectives.

```json theme={null}
{
  "role": "user",
  "content": [
    { "type": "text", "text": "Please describe the main content of this image" },
    {
      "type": "image_url",
      "image_url": {
        "url": "https://example.com/photo.jpg"
      }
    }
  ]
}
```

## Multiple Image Input

Multiple images can be submitted in a single request, allowing the model to integrate understanding from all images.

```json theme={null}
{
  "role": "user",
  "content": [
    { "type": "text", "text": "Compare the differences between these two images" },
    { "type": "image_url", "image_url": { "url": "https://example.com/a.jpg" } },
    { "type": "image_url", "image_url": { "url": "https://example.com/b.jpg" } }
  ]
}
```

***

## Image Clarity Control (detail Parameter)

The `detail` parameter can be used to control the level of detail the model applies when processing images:

| Parameter Value | Description                                             |
| :-------------- | :------------------------------------------------------ |
| `low`           | Low resolution, fast speed, low token consumption       |
| `high`          | High resolution, richer details, high token consumption |
| `auto`          | Automatically selects (default)                         |

```json theme={null}
{
  "image_url": {
    "url": "https://example.com/photo.jpg",
    "detail": "high"
  }
}
```

**Recommended Strategy:**

* Content understanding / scene judgment: `auto` or `low`
* When detail observation is needed (text, specific parts): `high`

***

## Billing and Token Explanation

Visual input will consume additional tokens, which should be considered in cost assessments:

* `low` mode: Each image consumes a fixed **85 tokens**
* `high` mode: Token consumption increases based on image size and resolution

**Recommendations:**

* Default to using `auto`
* Avoid unnecessary `high` in bulk or high-concurrency scenarios

## Usage Recommendations

* Always provide clear text instructions; do not send images alone.
* Control the number and resolution of images to avoid unnecessary costs.
* Conduct secondary validation for critical business outcomes.
* Use visual understanding as a supplementary capability, not the sole basis for judgment.

***

Última atualização: 2026-06-01