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Mistral Medium 3.1 vs Mistral Small 3.1 24B

We don't benchmark Mistral Medium 3.1 or Mistral Small 3.1 24B on Vision Evals. Mistral Medium 3.1 is proprietary under Proprietary: commercial use per vendor terms. Mistral Small 3.1 24B is open-weight under Apache 2.0: free for commercial use. Mistral Small 3.1 24B is cheaper per token ($0.35/M vs $0.40/M input). Test them side by side on your own images below.

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MistralMistral Medium 3.1
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Mistral Medium 3.1 vs Mistral Small 3.1 24B Comparison Table

Pricing updated November 10, 2026

PropertyMistral Medium 3.1Mistral Small 3.1 24B
OrganizationMistralMistral
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateAug 2025Mar 2025
Context Window128K128K
ParametersUnknown24B
LicenseProprietaryApache 2.0
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$0.400$0.351
Output $/1M$2.00$0.555
Vision Tasks
Both support 9 more tasks
Model Features
Both support 3 more features

Mistral Medium 3.1 vs Mistral Small 3.1 24B: Overview

Mistral Medium 3.1

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

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.