---
title: Llama 3.2 11B Vision — Multimodal LLM | ModelsLab
description: Integrate nim/meta/llama-3.2-11b-vision-instruct for image reasoning and VQA via API. Process text and images to generate precise text outputs. Start bu...
url: https://modelslab.com/nimmetallama-32-11b-vision-instruct
canonical: https://modelslab.com/nimmetallama-32-11b-vision-instruct
type: website
component: Seo/ModelPage
generated_at: 2026-05-05T18:46:01.367157Z
---

Available now on ModelsLab · Language Model

Nim/meta/llama-3.2-11b-vision-instruct
Vision Meets Language
---

[Try Nim/meta/llama-3.2-11b-vision-instruct](/models/together_ai/nim-meta-llama-3.2-11b-vision-instruct) [API Documentation](https://docs.modelslab.com)

Process Images with Text
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Multimodal Input

### Text and Images

Handle text+image inputs for text outputs using nim/meta/llama-3.2-11b-vision-instruct model.

Image Reasoning

### Visual Question Answering

Answer questions about images, charts, and documents with nim/meta/llama-3.2-11b-vision-instruct API.

Compact Power

### 11B Parameters

Deploy 11 billion parameter nim meta llama 3.2 11b vision instruct as nim/meta/llama-3.2-11b-vision-instruct alternative.

Examples

See what Nim/meta/llama-3.2-11b-vision-instruct can create
---

Copy any prompt below and try it yourself in the [playground](/models/together_ai/nim-meta-llama-3.2-11b-vision-instruct).

Chart Analysis

“<image>Analyze this sales chart. Identify the peak month and its value.”

Document QA

“<image>Extract key entities from this invoice image, including date, total, and items.”

Image Caption

“<image>Provide a detailed caption describing the scene, objects, and actions in this image.”

Visual Reasoning

“<image>What objects are present? Describe their positions and relationships.”

For Developers

A few lines of code.
Vision reasoning. One call.
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ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

- **Serverless:** scales to zero, scales to millions
- **Pay per token,** no minimums
- **Python and JavaScript SDKs,** plus REST API

[API Documentation ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())</code>
```

FAQ

Common questions about Nim/meta/llama-3.2-11b-vision-instruct
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[Read the docs ](https://docs.modelslab.com)

### What is nim/meta/llama-3.2-11b-vision-instruct?

It is an 11B parameter multimodal LLM from Meta that processes text and images for text output. Optimized for image reasoning, captioning, and VQA. Supports 128k context length.

### How does nim/meta/llama-3.2-11b-vision-instruct API work?

Send text and image inputs via API to get text responses. Uses vision adapter with cross-attention layers. Available through NIM for easy integration.

### What tasks suits nim meta llama 3.2 11b vision instruct?

Excels in document VQA, image captioning, visual reasoning, and chart analysis. Handles college-level math problems from images.

### Is nim/meta/llama-3.2-11b-vision-instruct LLM multilingual?

Supports English, French, German, Hindi, Italian, Portuguese, Spanish, Thai. Trained on 6B image-text pairs with December 2023 cutoff.

### Find nim/meta/llama-3.2-11b-vision-instruct alternative?

This NIM-optimized model offers compact vision capabilities versus larger 90B version. Ideal for edge deployment and API use.

### Access nim meta llama 3.2 11b vision instruct api?

Use standard LLM endpoints with image URLs in messages. Compatible with Together AI and similar platforms for inference.

Ready to create?
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Start generating with Nim/meta/llama-3.2-11b-vision-instruct on ModelsLab.

[Try Nim/meta/llama-3.2-11b-vision-instruct](/models/together_ai/nim-meta-llama-3.2-11b-vision-instruct) [API Documentation](https://docs.modelslab.com)

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*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-05-06*