第一项始终是图钉的来源。总体可信度是各份资料对上方所用开始和结束时间支持程度的加权平均;资料每比最新的一份旧 180 天,权重减半。
MiniMax 的新闻稿日期为 06.16.2025,其技术报告于 2025 年 6 月 16 日提交至 arXiv[6];Analytics Vidhya[3](6 月 18 日)称 M1 于 MiniMaxWeek 第 1 天发布,OpenRouter[4] 将 minimax/minimax-m1 列于 6 月 17 日(UTC)。
China Daily item on the Shanghai government site, 24 June 2025: MiniMax[1]-M1 is the world's first open-source, large-scale, hybrid-attention reasoning model with performance on par with top players, followed by the Hailuo 02 video model on 18 June and MiniMax Agent on 19 June.
Analytics Vidhya, 18 June 2025: M1 released on day 1 of the five-day MiniMaxWeek under Apache 2.0, 1M input and 80K output tokens, hands-on tests of an animation simulation, a web-search task and a logic puzzle, plus the MiniMax[1] Agent beta.
OpenRouter listing (read from its models API): MiniMax[1]: MiniMax M1, added 17 June 2025, 1,000,000-token context, $0.55 per million input and $2.20 per million output tokens.
Model card: 456B total and 45.9B active parameters, 1M-token context (8x DeepSeek R1), 40K and 80K thinking budgets, Apache 2.0, CISPO, about 25% of DeepSeek-R1 FLOPs at 100K tokens, and the full benchmark table with notes on SWE-bench and TAU-bench setups.[1]
有遗漏或错误吗?用你自己的话说明:能佐证此图钉的链接、不同的开始或结束日期及理由,或缺失、有误的信息。AI 会对照此图钉的来源进行核实,搜索更好的来源,并添加任何支持你说法的页面。图钉自身的来源仍然最重要。AI 也会查看图片:显示的是别的东西或显示效果差的图片会被移到后面或替换。
原标题: MiniMax-M1 Released
MiniMax-M1 模型卡中的结果表(括号内为思考预算);“--”表示未报告,“*”表示仅文本的 HLE 子集[5].
| 基准 | MiniMax-M1-80K | MiniMax-M1-40K | Qwen3-235B-A22B (32k) | DeepSeek-R1-0528 (64k) | DeepSeek-R1 (32k) | Seed-Thinking-v1.5 (32k) | Claude 4 Opus (64k) | Gemini 2.5 Pro 06-05 (64k) | OpenAI-o3 (100k) |
|---|---|---|---|---|---|---|---|---|---|
| AIME 2024 | 86.0 | 83.3 | 85.7 | 91.4 | 79.8 | 86.7 | 76.0 | 92.0 | 91.6 |
| AIME 2025 | 76.9 | 74.6 | 81.5 | 87.5 | 70.0 | 74.0 | 75.5 | 88.0 | 88.9 |
| MATH-500 | 96.8 | 96.0 | 96.2 | 98.0 | 97.3 | 96.7 | 98.2 | 98.8 | 98.1 |
| LiveCodeBench | 65.0 | 62.3 | 65.9 | 73.1 | 55.9 | 67.5 | 56.6 | 77.1 | 75.8 |
| FullStackBench | 68.3 | 67.6 | 62.9 | 69.4 | 70.1 | 69.9 | 70.3 | -- | 69.3 |
| GPQA Diamond | 70.0 | 69.2 | 71.1 | 81.0 | 71.5 | 77.3 | 79.6 | 86.4 | 83.3 |
| HLE(无工具) | 8.4* | 7.2* | 7.6* | 17.7* | 8.6* | 8.2 | 10.7 | 21.6 | 20.3 |
| ZebraLogic | 86.8 | 80.1 | 80.3 | 95.1 | 78.7 | 84.4 | 95.1 | 91.6 | 95.8 |
| MMLU-Pro | 81.1 | 80.6 | 83.0 | 85.0 | 84.0 | 87.0 | 85.0 | 86.0 | 85.0 |
| SWE-bench Verified | 56.0 | 55.6 | 34.4 | 57.6 | 49.2 | 47.0 | 72.5 | 67.2 | 69.1 |
| OpenAI-MRCR (128k) | 73.4 | 76.1 | 27.7 | 51.5 | 35.8 | 54.3 | 48.9 | 76.8 | 56.5 |
| OpenAI-MRCR (1M) | 56.2 | 58.6 | -- | -- | -- | -- | -- | 58.8 | -- |
| LongBench-v2 | 61.5 | 61.0 | 50.1 | 52.1 | 58.3 | 52.5 | 55.6 | 65.0 | 58.8 |
| TAU-bench(航空) | 62.0 | 60.0 | 34.7 | 53.5 | -- | 44.0 | 59.6 | 50.0 | 52.0 |
| TAU-bench(零售) | 63.5 | 67.8 | 58.6 | 63.9 | -- | 55.7 | 81.4 | 67.0 | 73.9 |
| SimpleQA | 18.5 | 17.9 | 11.0 | 27.8 | 30.1 | 12.9 | -- | 54.0 | 49.4 |
| MultiChallenge | 44.7 | 44.7 | 40.0 | 45.0 | 40.7 | 43.0 | 45.8 | 51.8 | 56.5 |