MiniMax: MiniMax M1
By Minimax
Released: 2025-06-17
Minimax Minimax M1, developed by MiniMax, features 45.9B parameters, MoE architecture and 1,000k-token context window. MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it to process long sequences, up to 1 million tokens, while maintaining competitive FLOP efficiency. With 456 billion total parameters and 45.9B active per token, this variant is optimised for complex, multi-step reasoning tasks. Trained via a custom reinforcement learning pipeline (CISPO), M1 excels in long-context understanding, software engineering, agentic tool use, and mathematical reasoning. Benchmarks show strong performance across FullStackBench, SWE-bench, MATH, GPQA, and TAU-Bench, often outperforming other open models like DeepSeek R1 and Qwen3-235B. Available at $0.4/1M tokens.
Visit MiniMax: MiniMax M1AI-Powered
Leverages advanced AI technology to deliver cutting-edge capabilities and results.
Fast & Efficient
Optimized performance ensures quick results without compromising on quality.
Purpose-Built
Specifically designed for llms tasks and workflows.
Minimax Model Timeline
33k tokens context
197k tokens context
197k tokens context
1,000k tokens context
1,000k tokens context
Specifications
AI Evaluation
Optimized for programming tasks, this model excels at code generation, debugging, and software engineering workflows with solid benchmark performance.
Pros
- Massive 1,000k token context
- Strong code generation and debugging
- Advanced logical reasoning
- Agent-ready with tool use
Cons
- May lack creative flair
- Speed/quality trade-off
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