- Edge & On-Device Developers: Build local intelligence agents for translation, assistants, or robotics with low latency.
- AI Engineers & Teams: Deploy efficient, capable reasoning models on consumer-grade GPUs (~24GB).
- Researchers & Benchmarkers: Use the instruct-tuned variant for robust chain-of-thought and function-calling tasks.
- Enterprises & Startups: Use the API-priced version for workflows needing high performance at low cost.
- Open-Source Advocates: Work with weights under Mistral Research License and tune for specialized needs.
How to Use Ministral 8B?
- Choose the Model: Use `ministral-8b-latest` via Mistral’s API or Hugging Face model card.
- Deploy via vLLM or Mistral-Inference: Recommended setups for local inference; vLLM currently supports 32K context.
- Implement Instruct Prompts: Use Mistral’s V3-Tekken template for user-assistant prompts; supports function calling.
- Optimize Inference: Use interleaved attention for memory efficiency and quantize to fit hardware constraints.
- Use in Production: API access at $0.10/million tokens; commercial license available for self-deployment.
- Edge-Class Efficiency: Designed for on-device use with low latency and efficient memory.
- Massive Context: Handles up to 128K tokens, supporting long input scenarios.
- Top-Small Model Performance: Achieves instruct scores of ~70.9 Arena, 76.8 HumanEval, and 54.5 on math—leading in the 8B category.
- Function Calling: Connects to external tools, enabling rich agentic workflows.
- Cost-Effective: At $0.10 per million tokens, it offers one of the best performance-to-cost ratios.
- High-quality instruct and benchmark performance in 8B size
- Supports long-context tasks with 128K tokens
- Efficient for edge and on-device deployment
- Function-calling enables agentic and tool-based applications
- Accessible API pricing with commercial license options
- vLLM limits context to 32 K until full support arrives
- Performance slightly lower than larger models in some coding tasks
- Research license may restrict some commercial use—requires separate commercial license
API only
$0.1/$0.1 per 1M tokens
$0.1 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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