Mistral Medium 3.1 vs Mistral Small 3.1 24B
Compare Mistral Medium 3.1 and Mistral Small 3.1 24B 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 Mistral Small 3.1 24B Comparison Table
Evals updated July 10, 2026Pricing updated July 16, 2026
| Property | Mistral Medium 3.1 | Mistral Small 3.1 24B |
|---|---|---|
| Organization | Mistral | Mistral |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Mar 2025 |
| Context Window | 128K | 128K |
| Parameters | 24B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.400 | $0.351 |
| Output $/1M | $2.00 | $0.555 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Multimodal Vision | ||
Mistral Medium 3.1 vs Mistral Small 3.1 24B: 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.
Mistral Small 3.1 24B, released on March 17, 2025, is an open-weight multimodal model from Mistral AI, distributed under the Apache-2.0 license. With around 24B parameters and a 128K token context window, it is available in both base and instruction-tuned (“Instruct”) variants. The model introduces vision support alongside text, enabling tasks like multimodal reasoning, captioning, and image-based Q&A.
It is multilingual, supporting many languages, and is optimized for fast responses, function calling, structured dialogue, and long-context reasoning. Despite its size, the model can be run locally in quantized formats, fitting on machines with ~32GB RAM, making it accessible to developers outside large cloud setups. However, the output length is smaller than the 128K input window, meaning long generations may require chaining. In addition, using full vision features or the maximum context window significantly increases compute costs, and performance on highly complex reasoning or enterprise-scale tasks still trails larger proprietary frontier models.
Frequently Asked Questions
Mistral Medium 3.1 is released under Proprietary, while Mistral Small 3.1 24B uses Apache 2.0. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.