- Developers & Engineers: Build chat assistants, document readers, or multimodal agents with vision and long-context capabilities.
- Researchers & Data Scientists: Analyze long documents, codebases, or images—leveraging chain-of-thought and multimodal reasoning.
- Enterprises & Startups: Deploy a powerful multimodal LLM on-premises or in cloud environments under Apache 2.0 license.
- Educators & Content Creators: Generate, edit, and understand multimodal content across languages and formats.
- Hobbyists & Open-Source Advocates: Run the model locally or in browsers with accessible hardware requirements.
How to Use Mistral Small 3.1?
- Choose Version: Download base or instruct checkpoints for `mistral-small-2503`.
- Deploy Locally or via API: Use Ollama, vLLM, or Hugging Face tools; also available on Google Cloud Vertex AI.
- Send Text or Vision Prompts: Input text, images, or combined media within a massive 128K-token window.
- Use for Tasks: Handle instruction-following, visual Q&A (e.g., ChartQA, DocVQA), long-document understanding, multilingual tasks.
- Optimize Performance: Fine-tune, quantize, or function-call within low-latency workflows.
- Multimodal Power: Integrates strong image understanding into a small, open-source model.
- 128K Context: Processes very long conversations, documents, or codebases seamlessly.
- High Benchmarks: Outperforms Gemma 3 and GPT-4o Mini in MMLU, Math, vision, and multilingual tests.
- Fast, Lightweight Deployment: Ideal for consumer-grade GPUs and MacBooks; 150 tokens/sec makes it responsive.
- Open, Commercial Use: Apache 2.0 license encourages wide adoption and modification for RAG, agents, and custom workflows.
- Full multimodal capabilities in a 24B model
- Huge 128K context window for long-form tasks
- Excellent benchmarks vs closed models
- Runs on accessible hardware with low latency
- Permissive Apache 2.0 license for commercial use
- Vision support is capped at inference-level image understanding
- Model size may still be heavy for mobile deployment
- May consume substantial memory when handling 128K contexts
API only
$0.5/$1.5 per 1M tokens
$1.5 per 1M output tokens
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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