单任务成本仅0.13美元！小米MiMo-V2.6-Pro将大模型带入0.1美元区间

广州日报

09/22

9月22日，小米发布并宣布开源全新一代MiMo大模型：全模态旗舰模型Xiaomi MiMo-V2.6-Pro、高效推理模型Xiaomi MiMo-V2.6-Flash，并同步上线MiMo-V2.6-Pro的超高速模式V2.6-Pro-Ultraspeed，以及MiMo Desktop 桌面客户端正式版。　　据全球权威大模型测评平台Artificial Analysis综合智能指数榜单显示，...

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Xiaomi's MiMo-V2.6-Pro brings its large model into the $0.10 range.","themeId":null,"isJumpTheme":false,"ttsUrl":"https://static.tigerbbs.com/664b04cdf1b0ab4ed0eb1c1a1d870d24","symbols_score_info":{"81810":0.6,"HXXD.SI":0.6,"DRGN":0.6,"MIUmain":0.6,"01810":1.97,"XIACY":1.97},"content_text":"9月22日，小米发布并宣布开源全新一代MiMo大模型：全模态旗舰模型Xiaomi MiMo-V2.6-Pro、高效推理模型Xiaomi MiMo-V2.6-Flash，并同步上线MiMo-V2.6-Pro的超高速模式V2.6-Pro-Ultraspeed，以及MiMo Desktop 桌面客户端正式版。　　据全球权威大模型测评平台Artificial Analysis综合智能指数榜单显示，Xiaomi MiMo-V2.6-Pro以46分的成绩登顶全球开源大模型第一、国产模型第一，超越Kimi K3、Qwen3.8 Max。值得一提的是，MiMo-V2.6-Pro首次支持文本、图片、视频、音频等全模态，成为全球开源模型阵容中罕见的全模态 Pro 模型。　　据悉，MiMo-V2.6系列已上线 Xiaomi MiMo 开放平台，并继续沿用V2.5系列的 API 定价。Artificial Analysis智能指数单任务成本榜单数据显示，小米MiMo-V2.6-Pro实测单任务成本为0.13美元，首次将全球前沿智能带入0.1美元成本区间。　　价格不变、智能翻倍　　经过大规模、多任务RL后训练，MiMo-V2.6-Pro在大多数 Agent Benchmark 上取得了优异表现，在权威大模型测试平台 Artificial Analysis综合智能指数中取得 46分，成为当前最强的开源模型之一。 　　在智能水平跻身全球第一梯队的同时，MiMo-V2.6 系列维持了价格不变，其中MiMo-V2.6-Pro的单位任务成本维持在0.13美元，在同等智能水平下，价格仅为海外模型的 1/20 至 1/60。 　　价格不变、智能翻倍，让MiMo-V2.6-Pro将行业关注的“智能-成本”帕累托最优曲线大幅向外推进，成为同等价格下的智能最优选之一。在Artificial Analysis智能指数单任务成本榜单中，MiMo-V2.6-Pro综合智能指数仅次于GPT-6Astra、Claude opus5、Fable 5.1 (max)以及Muse Spark 1.3 (max)等顶级闭源模型，成为全球开源阵容的新领军者。　　参数不变、能力跨代　　据了解，小米MiMo-V2.6系列预训练架构和参数规模与上一代保持不变，却实现了智能的代际跃升，Coding、Agent、Design、Research、Cyber能力全面增强。　　最值得关注的突破，是MiMo-V2.6-Pro开始以“Co-Scientist”的角色参与真实科研。　　在小米前沿材料研究团队的多轮提示与交互下，MiMo-V2.6-Pro协助研究人员开展材料设计与计算筛选，提出数种金属有机框架（MOF）材料方案，用于吸附被称为“永久性污染物”的全氟和多氟烷基物质（PFAS）。　　模型不仅完成相关文献与专利梳理、研究假设提出和方案新颖性评估，还调用开源计算工具搭建模拟环境，计算候选材料与PFAS之间的结合强度，为后续实验验证筛选潜在方案。整个过程中，MiMo-V2.6-Pro帮助研究团队将工作效率提升约10倍，把原本约一个月的研发周期缩短至2—3天。　　“它在这次任务中展现出的研究能力，我认为已经达到了训练有素的博士研究人员的水平。”北京大学材料科学与工程学院特聘研究员、博士生导师窦锦虎表示，“这些候选材料值得进一步开展实验验证，我也期待MiMo在更广泛的材料探索中提出有价值的新思路。”　　此外，MiMo-V2.6-Pro还协助研究人员，在Lean 4中完成Li–Yorke经典论文《周期三蕴含混沌》原始主定理的完整形式化。该定理表明，对于连续的区间自映射，一个三周期轨道的存在，足以推出所有正整数周期的轨道以及一个不可数的混沌集。　　在研究人员设计的探索策略引导下，MiMo-V2.6-Pro通过多个Subagent协作推进定理陈述与证明的形式化。经修订与整合，项目最终形成6000余行Lean源码，全部证明均通过Lean内核核验，不含任何未完成证明占位。　　值得注意的是，MiMo-V2.6-Pro此前并未接受针对Lean的专项后训练。这一成果显示，其通用推理、代码生成和多智能体协作能力，已经能够参与高复杂度、可严格验证的数学研究任务。　　在科研之外，依托图像、视频、音频和文本的原生全模态理解能力，MiMo-V2.6进一步拓展至3D世界构建、Blender建模、具身智能和数字内容创作等场景，可完成交互式游戏生成、3D资产制作、机械臂控制，以及网页、Figma、幻灯片、视频和音乐创作；其设计能力在Design Arena评测中已接近Claude Opus 5和GPT-5.6 Sol。　　规模扩展、成果开源　　9月17日，小米MiMo通过直播方式分享MiMo-V2.6系列强化学习（RL）后训练全流程，在行业内引起了广泛关注。　　据小米方面透露，MiMo-V2.6 RL后训练耗时约6天，其中Flash版本训练成本约85万美元，Pro版本约262万美元，两个版本合计训练成本超过347万美元，累计约 75 万条轨迹。在长程软件工程能力评测中，Flash版本得分从48.8提升至65.68，Pro版本从58.4提升至72.57，提升幅度分别为17分和14分。 　　小米方面表示，随着训练规模扩大，团队冻结 Router 以抑制训练漂移，并建立覆盖奖励设计、对抗性评测、异常检测和验证器交叉校验的 Reward Hacking 防线，提升训练稳定性与奖励可靠性。　　据了解，此次小米除了开源MiMo-V2.6-Pro 和 Flash模型权重以外，也将上述RL技术成果和配套资源全面开源，开放了 MiMo-V2.6-Distill-Qwen-9B 和配套 RL训练最佳实践，包含7K+高质量任务环境、端到端RL训练框架和轻量化可组合的Agent框架。　　以前述高质量任务为例，在MiMo的训练实践中，以 MiMo-V2.6-Distill-Qwen-9B 为起点展开 RL 训练，在 11 项评测上全面提升：SWE-bench Verified 从 61.1 提升至 66.2，MiMo Cyber Bench 从 31.3 提升至 47.0，Terminal Bench 2.1 从 37.1 提升至 52.8，MiMo Visual Coding 从 64.0 提升至 72.4。　　小米MiMo相关负责人表示，希望通过技术成果的全面开放，帮助更多研究者复现和验证相关结果，持续推动规模化RL技术及研究进步。（文章来源：广州日报）","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":false,"editions":null,"column":"","sentiment":"0","news_tag":"productRelease","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":1,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"}}}