MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention — part 4/4

Cleaned-text code points [47031, 53092). Bibliography omitted here; source.md includes it.

Highlights in Complex Scenarios: Software Engineering, Long Context, and Tool use. Benefiting from our execution-based, software engineering environments during RL, MiniMax-M1-40k and MiniMax-M1-80k achieve strong scores of 55.6% and 56.0% on SWE-bench verified respectively. These results are slightly inferior to DeepSeek-R1-0528’s 57.6% and significantly surpass other open-weights models. Leveraging its 1M context window, the M1 models significantly outperform all other open-weight models in long-context understanding. They even surpass OpenAI o3 and Claude 4 Opus, ranking second globally and trailing only Gemini 2.5 Pro by a small margin. In agentic tool-use scenarios (TAU-bench), MiniMax-M1-40k surpasses all open-weight models and even Gemini-2.5 Pro. Moreover, MiniMax-M1-80k consistently outperforms MiniMax-M1-40k across most benchmarks, confirming the benefits of scaling test-time compute.

### 6.2 Effect of RL Scaling

To investigate the effect of RL scaling, we track performance and response length throughout training. Figure 4 presents three representative examples from AIME 2024, AIME 2025, and LiveCodeBench v5, respectively. We observe consistent improvements in both model performance and response length during training. Notably, average response lengths on AIME and LiveCodeBench exceed 20,000 tokens, with AIME 2024 accuracy showing substantial gains from 68% to 80%. Crucially, the strong correlation between accuracy gains and increased response length in these visualizations underscores the importance of extending RL scaling to facilitate more extensive reasoning processes.

## 7 Conclusion and Future work

In this work, we introduce and release MiniMax-M1, the world’s first open-weight, large-scale reasoning model featuring a lightning attention mechanism. This efficient attention design enables MiniMax-M1 to natively support inputs of up to 1M tokens and generation lengths of 80K tokens—both significantly exceeding capabilities of other open-weight models. These capabilities render MiniMax-M1 uniquely suited for complex, realistic scenarios requiring long context and extended reasoning, properties empirically validated by its strong performance on software engineering, agentic tool use, and long-context understanding benchmarks. Beyond the inherent efficiency advantages of lightning attention for RL training, this work contributes a novel RL algorithm, CISPO, to accelerate training. Combining architectural advantages with CISPO, we efficiently trained MiniMax-M1, with complete RL training completed in three weeks using 512 H800 GPUs. Across comprehensive evaluations, MiniMax-M1 ranks among the world’s best open-weight models alongside DeepSeek-R1 and Qwen3-235B.

Looking forward, as test-time compute continuously scales to power increasingly complex scenarios, we foresee significant potential for such efficient architectures in addressing real-world challenges. These include automating company workflows ( Xu et al., 2025 ) and conducting scientific research ( Si et al., 2024 ; OpenAI, 2025 ) . Real-world applications particularly demand LRMs that function as agents interacting with environments, tools, computers, or other agents—requiring reasoning across dozens to hundreds of turns while integrating long-context information from diverse sources. We envision MiniMax-M1 serving as a strong foundation for such applications with unique advantages, and we are fully dedicated to further evolving MiniMax-M1 toward this goal.

## Appendix A Contributors

The contributors to the report are listed in alphabetical order as follows:

Aili Chen, Aonian Li, Bangwei Gong, Binyang Jiang, Bo Fei, Bo Yang, Boji Shan, Changqing Yu, Chao Wang, Cheng Zhu, Chengjun Xiao, Chengyu Du, Chi Zhang, Chu Qiao, Chunhao Zhang, Chunhui Du, Congchao Guo, Da Chen, Deming Ding, Dianjun Sun, Dong Li, Enwei Jiao, Haigang Zhou, Haimo Zhang, Han Ding, Haohai Sun, Haoyu Feng, Huaiguang Cai, Haichao Zhu, Jian Sun, Jiaqi Zhuang, Jiaren Cai, Jiayuan Song, Jin Zhu, Jingyang Li, Jinhao Tian, Jinli Liu, Junhao Xu, Junjie Yan, Junteng Liu, Junxian He, Kaiyi Feng, Ke Yang, Kecheng Xiao, Le Han, Leyang Wang, Lianfei Yu, Liheng Feng, Lin Li, Lin Zheng, Linge Du, Lingyu Yang, Lunbin Zeng, Minghui Yu, Mingliang Tao, Mingyuan Chi, Mozhi Zhang, Mujie Lin, Nan Hu, Nongyu Di, Peng Gao, Pengfei Li, Pengyu Zhao, Qibing Ren, Qidi Xu, Qile Li, Qin Wang, Rong Tian, Ruitao Leng, Shaoxiang Chen, Shaoyu Chen, Shengmin Shi, Shitong Weng, Shuchang Guan, Shuqi Yu, Sichen Li, Songquan Zhu, Tengfei Li, Tianchi Cai, Tianrun Liang, Weiyu Cheng, Weize Kong, Wenkai Li, Xiancai Chen, Xiangjun Song, Xiao Luo, Xiao Su, Xiaobo Li, Xiaodong Han, Xinzhu Hou, Xuan Lu, Xun Zou, Xuyang Shen, Yan Gong, Yan Ma, Yang Wang, Yiqi Shi, Yiran Zhong, Yonghong Duan, Yongxiang Fu, Yongyi Hu, Yu Gao, Yuanxiang Fan, Yufeng Yang, Yuhao Li, Yulin Hu, Yunan Huang, Yunji Li, Yunzhi Xu, Yuxin Mao, Yuxuan Shi, Yuze Wenren, Zehan Li, Zelin Li, Zhanxu Tian, Zhengmao Zhu, Zhenhua Fan, Zhenzhen Wu, Zhichao Xu, Zhihang Yu, Zhiheng Lyu, Zhuo Jiang, Zibo Gao, Zijia Wu, Zijian Song, Zijun Sun

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