Mistral Medium 3.1 vs Pixtral 12B
Compare Mistral Medium 3.1 and Pixtral 12B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Mistral Medium 3.1 vs Pixtral 12B Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Mistral Medium 3.1 | Pixtral 12B |
|---|---|---|
| Organization | Mistral | Mistral |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Sep 2024 |
| Context Window | 128K | 128K |
| Parameters | 12B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.400 | |
| Output $/1M | $2.00 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Multimodal Vision | ||
Mistral Medium 3.1 vs Pixtral 12B: Overview
Mistral Medium 3.1, released in August 2025 as the mistral-medium-2508 update, is a proprietary frontier model from Mistral AI positioned between smaller open models and high-end closed LLMs. It is multimodal, handling both text and image inputs, with a context window of ~128K tokens.
Compared to Mistral Medium 3.0, the 3.1 release introduces improvements in reasoning, coding, STEM, and enterprise workflows, along with better tone control for conversational and business applications. It is designed for scalable enterprise deployments, including hybrid cloud and on-premises VPC setups. As part of Mistral’s Premier line, Medium 3.1 is a commercial-only offering: while it delivers strong accuracy and performance, trade-offs include higher costs than open-weight models, restricted fine-tuning access, and increased latency/cost for very large contexts.
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.