MiniMax‑M1 Debuts with Cost‑Efficient, High-Performance RL Model
MiniMax-AI has released MiniMax‑M1, a large open-weight AI model tuned for long-context reasoning and software engineering tasks. Built using hybrid attention and a novel reinforcement learning algorithm, it was trained in just three weeks for under $535K. Its public release offers developers a new long-context contender at an unusually efficient cost.
MiniMax‑M1 comes in two variants with “thinking budgets” of 40K and 80K tokens, optimizing for different task complexities. The model employs Lightning Attention and a hybrid attention mechanism, along with a new RL fine-tuning strategy called CISPO. The result is a model that shows strong comparative performance against top-tier open-weight peers like DeepSeek‑R1 and Qwen3‑235B. Training took place across 512 H800 GPUs, reaching completion in just under three weeks at a compute cost of $534,700. This puts MiniMax‑M1 among the most cost-efficient efforts in the 100B+ parameter class. The model particularly excels in tasks requiring long-context comprehension and complex reasoning in code, positioning it as a useful tool for AI engineers and researchers building on transformer backbones. Its public release via GitHub marks a deliberate open-access stance, contrasting with more closed models from enterprise labs. For developers needing long-context handling and for teams exploring new RL fine-tuning strategies, MiniMax‑M1 offers a compelling open-source option with competitive performance and efficient scaling.
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