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Llama 3.2 11B Vision Instruct

Llama 3.2 11B Vision Instruct is Meta's entry point for vision-language capability in the Llama 3.2 family. This 11B parameter model adds image understanding through a cross-attention adapter, making it an accessible starting point for multimodal applications.

Tool UseVision (Image)
index.ts
import { streamText } from 'ai'


const result = streamText({
  model: 'meta/llama-3.2-11b',
  prompt: 'Why is the sky blue?'
})
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Frequently Asked Questions

  • How does the cross-attention adapter architecture differ from native multimodal designs?

    The adapter connects a vision encoder to the existing Llama 3.1 8B language model via cross-attention layers, with the language model weights frozen. This preserves text generation quality while adding vision capability.

  • What kinds of images does Llama 3.2 11B Vision Instruct handle well?

    Charts, diagrams, documents with visual elements, product images, and standard photographic content all fall within its capability range. The model reads text in images, describes scenes, and answers questions about visual content at the 11B scale.

  • How does Llama 3.2 11B Vision Instruct compare to Llama 3.2 90B for vision tasks?

    90B provides higher visual reasoning capacity for complex scenes, multi-image analysis, and tasks that pair deep visual understanding with large-scale language generation. 11B is cheaper to serve, with a lower ceiling on visual reasoning.

  • What is the context window for Llama 3.2 11B Vision Instruct?

    128K tokens, sufficient for extended visual conversations and moderate document analysis tasks.

  • How do I use Llama 3.2 11B Vision Instruct on AI Gateway?

    Use the identifier meta/llama-3.2-11b with any supported interface. You can combine image and text inputs in the same request. Send the request through AI Gateway; it routes to providers automatically.