# minimax-m1

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 optimized 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.

## Model Information

- **Organization**: [Minimax](/llm.txt)
- **Slug**: minimax-m1
- **Available at Providers**: 11
- **Release Date**: June 17, 2025

## Providers

| Provider | Name | $ Input (per 1M) | $ Output (per 1M) | Free | Link |
|----------|------|-----------------|------------------|------|------|
| [Nano-GPT](/llm/nanogpt.txt) | MiniMax M1 |  |  |  |  |
| [OpenRouter](/llm/openrouter.txt) | MiniMax M1 | 0.40 | 2.20 |  | [View](https://openrouter.ai/minimax/minimax-m1) |
| [ValorGPT](/llm/valorgpt.txt) | MI |  |  |  | [View](https://www.valorgpt.com/models/minimax-minimax-m1) |
| [Yupp](/llm/yupp.txt) | MiniMax M1 (OpenRouter) |  |  |  |  |
| [LangDB](/llm/langdb.txt) | minimax-m1 |  |  |  | [View](https://langdb.ai/app/models) |
| [302.AI](/llm/302ai.txt) | MiniMax-M1 | 0.13 | 1.25 |  |  |
| [Kilo Code](/llm/kilocode.txt) | MiniMax: MiniMax M1 | 0.40 | 2.20 |  |  |
| [Blackbox AI](/llm/blackboxai.txt) | MiniMax: MiniMax M1 | 0.30 | 1.65 |  |  |
| [Arena AI](/llm/arenaai.txt) |  |  |  |  |  |
| [Qiniu](/llm/qiniuai.txt) | MiniMax M1 |  |  |  |  |
| [WaveSpeed AI](/llm/wavespeed.txt) | minimax-m1 | 0.44 | 2.42 |  |  |

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[← Back to all providers](/llm.txt)