MiniMax-M1 (M1-40k / M1-80k)

// Overview

MiniMax-M1 is, per MiniMax, the world's first open-weight, large-scale hybrid-attention reasoning model. It is built on the MiniMax-Text-01 base and uses a Mixture-of-Experts design with 456 billion total parameters, of which 45.9 billion are activated per token across 32 experts. Its defining trait is a hybrid attention stack: a softmax-attention transformer block follows every seven 'lightning' (linear) attention blocks, which gives near-linear cost as the sequence grows. MiniMax reports that at a 100K-token generation length M1 uses roughly 25% of the FLOPs that DeepSeek-R1 would.

M1 ships in two variants that differ only in their reasoning (thinking) budget: MiniMax-M1-40k and MiniMax-M1-80k. Both natively support a 1-million-token context window — eight times that of DeepSeek-R1 and on par with closed models like Gemini 2.5 Pro. The 80k variant generally scores a little higher on reasoning and coding, while the 40k variant is cheaper to run and actually leads on some long-context and agent benchmarks. Both were trained with reinforcement learning using MiniMax's CISPO algorithm, which clips importance-sampling weights rather than token updates; the full RL run took 512 H800 GPUs about three weeks at a reported rental cost of $534,700.

The weights are released under the permissive Apache 2.0 license on Hugging Face and GitHub, and MiniMax recommends vLLM (0.9.2+) or Transformers for deployment. M1 is text-only. It is positioned for long-context understanding, software-engineering tasks, and agentic tool use, where MiniMax reports it tops other open-weight models and is competitive with leading proprietary systems.

// Benchmarks

1. AIME 202486%
2. AIME 2024 (M1-40k)83.3%
3. AIME 202576.9%
4. LiveCodeBench65%
5. SWE-bench Verified56%
6. SWE-bench Verified (M1-40k)55.6%
7. MMLU-Pro81.1%
8. GPQA Diamond70%
9. TAU-bench (airline, M1-80k)62%
10. TAU-bench (retail, M1-40k)67.8%
11. OpenAI-MRCR (1M, M1-40k)58.6%
12. LongBench-v261.5%

Scores on a 0–100 scale (25-point gridlines); higher is better. Each benchmark links to its published source.

// Pricing

Tiered input pricing by context length from MiniMax's official announcement; OpenRouter lists $0.40 in / $2.20 out. Free unlimited use is offered on the MiniMax app and web.

Pricing source ↗

// Strengths

• Native 1M-token context — among the largest of any open-weight model, matching closed frontier models
• Efficient long-form reasoning: lightning attention cuts FLOPs to ~25% of DeepSeek-R1 at 100K-token generation
• Strong agentic tool use — leads open-weight models on TAU-bench and beats Gemini 2.5 Pro on parts of it
• Apache 2.0 license with fully open weights — free commercial use and self-hosting
• Two thinking budgets (40k/80k) let you trade reasoning depth against cost
• Competitive coding and math (AIME 2024 86.0%, SWE-bench Verified 56.0% on the 80k variant)

// Best for

• Long-document and whole-codebase analysis that needs hundreds of thousands of tokens of context
• Agentic tool-use and function-calling workflows
• Software engineering: bug fixing and repo-level tasks (SWE-bench-style)
• Math and competition-style reasoning
• Self-hosted reasoning deployments where an open Apache-2.0 license is required

// How to access