Llama 3.2 Vision 11b vs Pixtral 12B
Compare Llama 3.2 Vision 11b and Pixtral 12B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Llama 3.2 Vision 11b vs Pixtral 12B Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Llama 3.2 Vision 11b | Pixtral 12B |
|---|---|---|
| Organization | Meta | Mistral |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2024 | Sep 2024 |
| Context Window | 128K | 128K |
| Parameters | 11B | 12B |
| License | Proprietary | Apache 2.0 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Model Features | ||
| Multimodal Vision | ||
Llama 3.2 Vision 11b vs Pixtral 12B: Overview
Llama 3.2 Vision 11B, released by Meta on September 25, 2024, is the first mid-sized model in the Llama family with vision capabilities, supporting both text and image inputs with text-only outputs. It contains around 11 billion parameters (~10.6B) and features a 128,000-token context window, making it suitable for multimodal reasoning over long documents and image-text tasks. The model was trained on ~6 billion image–text pairs and has a knowledge cutoff of December 2023.
The model is available in a base and an instruction-tuned (“Vision-Instruct”) version, optimized for tasks like captioning, visual question answering, and image reasoning. It leverages Group-Query Attention (GQA) for improved inference efficiency and scalability. While text tasks officially support multiple languages (English, German, French, Italian, Portuguese, Hindi, Spanish, Thai), multimodal (image+text) tasks are supported primarily in English. Llama 3.2 Vision 11B is accessible through Hugging Face, Amazon Bedrock, Azure AI Foundry, NVIDIA NIM, and OCI, making it a widely deployable open-weight multimodal foundation model.
Pixtral-12B is a vision-language model introduced by Mistral AI in September 2024 under the Apache 2.0 license, designed to process both text and images in a unified context. With ~12 billion parameters in its decoder and an additional ~400 million in a custom-trained vision encoder, it supports long-context reasoning up to 128k tokens and accepts multiple images per input. Its architecture is optimized for handling variable image sizes and aspect ratios, making it flexible for diverse multimodal tasks.
As Mistral’s first VLM, Pixtral-12B delivers strong performance not only on image-text reasoning benchmarks but also in text-only applications, positioning it as a versatile alternative to models like GPT-4V and LLaVA. Its open availability via Hugging Face and major cloud providers such as Amazon Bedrock and SageMaker makes it accessible for research and production. Typical use cases include document analysis, visual QA, data extraction, and multimodal assistants requiring both textual and visual understanding.