MiniMax-M1(News): Open-Source AI with 1M Token Context - GitHub Release (2025)!
MiniMax-M1 is a groundbreaking open-weight hybrid-attention AI model developed by MiniMax AI. It is one of the first AI models to offer a context window of up to 1 million tokens, making it ideal for long-form reasoning, code agents, and complex document processing.
GitHub Repository Overview:
Hosted at github.com/MiniMax-AI/MiniMax-M1, the official repository provides:
- Apache-2.0 licensed model weights
- Configuration files for vLLM and Hugging Face Transformers
- Deployment guides and benchmarks
Model Features:
- Hybrid Attention Architecture: Combines Mixture-of-Experts (MoE) with Lightning Attention
- Long Context Support: Handles input sizes up to 1 million tokens
- Efficient Inference: Operates at only 25–30% the FLOPs of comparable models like DeepSeek R1
Performance and Benchmarks:
MiniMax-M1 performs competitively across key evaluations such as AIME, SWE-bench, and GPQA. It achieves high scores in reasoning and software engineering tasks while maintaining a lower computational footprint.
Training and Cost Efficiency:
The model was trained using 512 NVIDIA GPUs over three weeks, with a total estimated cost of approximately $534,700.
Deployment Options:
- Use with vLLM for scalable serving
- Compatible with Hugging Face Transformers
- API support via OpenRouter
Real-World Applications:
- Document analysis agents
- Code generation and debugging assistants
- Enterprise deployments with data privacy control
How to Download and Use?
- Visit: MiniMax-M1 on GitHub
- Clone the repository
- Follow the documentation for vLLM or Transformers setup
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Conclusion
MiniMax-M1 is a major open-source milestone for developers, researchers, and tech enthusiasts. With long context support, strong benchmarks, and efficient training cost, it provides a highly accessible option for powerful AI workloads in 2025 and beyond.