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Mistral Small 3.1

Mistral's 24B multimodal open-source model — beats GPT-4o Mini, Apache 2.0

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freemiumDR 86Open weights under Apache 2.0 license — free to download, self-host, and use commercially. Available via Mistral API (La Plateforme) and Le Chat.View full pricing →

Visit Mistral Small 3.1

https://mistral.ai/news/mistral-small-3-1

About Mistral Small 3.1

Mistral Small 3.1, released March 17, 2025, is a 24B open-source model that adds multimodal vision and a 128k context window to Mistral Small 3. Apache 2.0 license. Outperforms Gemma 3 and GPT-4o Mini on text and multimodal benchmarks at 150 tokens/second. Runs on a single RTX 4090 or Mac with 32GB RAM — making it the first open-source small model to surpass leading proprietary competitors across text, vision, multilingual, and long-context tasks.

Key Features

24B parameter model (Mistral-Small-3.1-24B)
128k token context window
Native multimodal vision — text and image inputs in one model
150 tokens/second inference speed
Outperforms Gemma 3 and GPT-4o Mini on GPQA Diamond and text benchmarks
Apache 2.0 license — permissive commercial and self-hosted use
Multilingual support across 40+ languages
Runs on a single RTX 4090 or Mac with 32GB RAM
Available as base and instruct checkpoints for fine-tuning
Function calling and agentic workflow support

Mistral Small 3.1 Pros & Cons

Pros

  • +First open-source small model to outperform GPT-4o Mini and Gemma 3 on both text and multimodal benchmarks
  • +Apache 2.0 license — no commercial restrictions, fully open for fine-tuning and redistribution
  • +Runs on consumer hardware (RTX 4090 or 32GB Mac) — no expensive GPU cluster needed
  • +128k context window enables long-document and agentic tasks at the small-model tier
  • +150 tokens/second inference speed is fast enough for real-time applications
  • +Both base and instruct checkpoints released — strong starting point for fine-tuning specialized models

⚠️ Cons

  • Superseded by Mistral Small 4 (March 2026) which is faster, larger, and adds reasoning
  • Vision capabilities, while present, are not as polished as GPT-4o Vision for complex document tasks
  • No proprietary API inference at the scale of OpenAI — Mistral API capacity can lag during high-demand periods
  • 24B still requires a capable GPU — not practical for edge or mobile deployment

Who Is Mistral Small 3.1 Best For?

👤Developers wanting a capable open-source multimodal model they can self-host or fine-tune
👤Regulated industries needing on-premise deployment with a permissive license
👤Teams building long-context pipelines where GPT-4o Mini's 128k context is too limiting
👤Researchers building reasoning models on top of an open-source foundation (e.g. DeepHermes-style fine-tunes)

Tags

mistralllmapiopen-sourceopen-weightsmultimodalvisionself-hosted
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