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

# Image understanding

> Use vision-capable models to read images, answer questions, compare content, and extract structured information.

Image understanding lets a model read images and return text. You can send screenshots, photos, product images, receipts, charts, or design drafts, then ask the model to describe, compare, classify, or extract information.

<Info>
  For OpenAI-compatible calls, image understanding usually uses `chat/completions` with both text and image input. Available models depend on the vision labels shown in the model catalog.
</Info>

## Use cases

* Screenshot QA: explain an app screen, console page, error screenshot, or product UI.
* Document extraction: extract amount, date, vendor, or fields from receipts and invoices.
* Product and asset review: identify objects, colors, style, composition, and usage context.
* Chart understanding: summarize a chart trend or explain visual differences.

## Suggested workflow

<Steps>
  <Step title="Choose a vision model">
    Filter for models that support image input and copy the model name.
  </Step>

  <Step title="Prepare image input">
    Provide an accessible image URL or Base64 image data according to model requirements.
  </Step>

  <Step title="Ask for a specific result">
    Tell the model whether you need a description, comparison, field extraction, or JSON output.
  </Step>
</Steps>

<Tip>
  For reliable field extraction, combine image understanding with [structured output](/en/guide/structured-output).
</Tip>

## Next steps

* [Vision and image generation](/en/guide/vision-and-image)
* [Structured output](/en/guide/structured-output)