[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-mistral-medium-3-5-128b-dense-merged-swe-77-6":3,"topics-all":36,"news-related-1663a918-0178-4d4b-b54b-b26b289860a5":55},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":29,"published_at":30,"created_at":31,"modified_at":32,"is_published":33,"publish_type":34,"image_url":13,"view_count":35},"1663a918-0178-4d4b-b54b-b26b289860a5","Mistral Medium 3.5：首款合并旗舰模型，77.6% SWE-Bench 进入第一梯队","Mistral AI 于 4 月 29 日发布 Medium 3.5，这是其首款合并旗舰模型——将指令遵循、推理、代码三大能力统一在单一 128B dense 模型中，不再需要维护多套专精模型，部署与路由复杂度大幅降低。\n\n核心技术规格：1280 亿全量激活参数（非 MoE），256K 上下文窗口，自研视觉编码器支持可变分辨率图片输入。推理 effort 可按请求配置——简单问答走快速路径，复杂代码任务切换深度推理模式，同一模型覆盖两类场景。\n\nBenchmark 表现值得关注。SWE-Bench Verified 达到 77.6%，已非常接近 Gemini 3.1 Pro Preview 的 78.8%。对于真实 GitHub issue 修复任务，Medium 3.5 的表现已经能够进入第一梯队。Tau3-Telecom 91.4% 则展示了其领域专项 agent 能力。\n\n对比前代产品：Medium 3.1 是纯文本模型，Magistral 负责推理，Devstral 2 专精代码——三条路并行走了一年，如今合并为一条路。从产品策略看，这是务实的技术收敛：从模型矩阵转向统一底座，用单一可配置模型承接更多场景。\n\n开源策略延续性也值得关注。Medium 3.5 采用修改版 MIT 协议（开放权重，商业可用，营收例外条款），支持自托管（四卡 H100 即可）。.5\u002F7.5 每百万 token 的 API 定价也低于同类竞品。\n\n对开发者而言，这意味着在 GPT-5.5 和 Claude Opus 4.7 之外，多了一个高性价比的开源选择。特别是在需要兼顾推理深度与部署成本的中型团队场景，128B dense 模型的恰好够用属性正在成为新的甜蜜点。","https:\u002F\u002Fdocs.mistral.ai\u002Fmodels\u002Fmodel-cards\u002Fmistral-medium-3-5-26-04","2436174c-644b-4a65-9a98-e7a3b705569a",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"e82b2d09-81b2-43d1-977e-e018443b3c14","coding-agent",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":18,"name":19,"slug":19,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"14984960-95e2-4fdc-98a0-89bcd726bb86","en","Mistral Medium 3.5: first merged flagship, 77.6% SWE-Bench","Mistral AI released Medium 3.5 on April 29, its first merged flagship model — unifying instruction following, reasoning, and code capabilities in a single 128B dense model, no longer needing to maintain multiple specialized models, dramatically reducing deployment and routing complexity.\n\nCore technical specs: 128 billion fully-activated parameters (not MoE), 256K context window, self-developed vision encoder supporting variable-resolution image input. Inference effort can be configured per request — simple Q&A takes the fast path, complex code tasks switch to deep reasoning mode, the same model covering both scenarios.\n\nBenchmark performance is worth attention. SWE-Bench Verified reached 77.6%, very close to Gemini 3.1 Pro Preview's 78.8%. For real GitHub issue fixing tasks, Medium 3.5's performance has entered the top tier. Tau3-Telecom 91.4% demonstrates its domain-specific agent capability.\n\nCompared to predecessors: Medium 3.1 was a pure-text model, Magistral handled reasoning, Devstral 2 specialized in code — three parallel paths for a year, now merged into one. From a product strategy perspective, this is pragmatic technical convergence: moving from model matrix to unified base, using a single configurable model to cover more scenarios.\n\nOpen-source strategy continuity is also worth attention. Medium 3.5 adopts a modified MIT license (open weights, commercial use, revenue exception clause), supports self-hosting (four H100s suffice). API pricing of $0.5\u002F1.5 per million tokens is also lower than comparable competitors.\n\nFor developers, this means another high-cost-performance open-source option besides GPT-5.5 and Claude Opus 4.7. Especially in mid-team scenarios needing both reasoning depth and deployment cost, the \"just right\" nature of 128B dense models is becoming a new sweet spot.","mistral-medium-3-5-128b-dense-merged-swe-77-6","2026-04-30T13:06:00Z","2026-04-30T13:08:20.829514Z","2026-08-19T02:08:40.142862Z",true,"agent",271,[37,46],{"slug":38,"tag_slug":38,"title_zh":39,"title_en":40,"intro_zh":41,"intro_en":42,"id":43,"is_active":33,"created_at":44,"modified_at":45},"ai-for-science","AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":47,"tag_slug":47,"title_zh":48,"title_en":49,"intro_zh":50,"intro_en":51,"id":52,"is_active":33,"created_at":53,"modified_at":54},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":56},[57,62,67,72,77,82],{"id":58,"title":59,"news_slug":60,"published_at":61},"4d436945-18e9-4d69-a4c8-c1e3e975ab33","MiniMax M3发布：稀疏注意力打通百万token上下文，开源模型编程能力逼近闭源前沿","minimax-m3-sparse-attn-million-token-msa","2026-06-04T01:00:00+00:00",{"id":63,"title":64,"news_slug":65,"published_at":66},"5736961c-9502-40d0-a39b-27f8ceb4380a","AI造AI:Recursive开源笔记本编程模型Flint","recursive-flint-laptop-coding-model","2026-10-22T19:05:00+00:00",{"id":68,"title":69,"news_slug":70,"published_at":71},"ace7a537-e2de-448f-811f-a028357aed42","FocusGUI:给全模态GUI智能体装上注意力滤镜","focusgui-omni-modal-gui-agents","2026-10-21T19:05:00+00:00",{"id":73,"title":74,"news_slug":75,"published_at":76},"a491d4db-2d5a-4723-b093-28d1512fd647","VLA-Δ:物理状态变化当监督信号,LIBERO 98.4","vla-delta-physical-state-transition","2026-10-20T19:07:27+00:00",{"id":78,"title":79,"news_slug":80,"published_at":81},"b44fb160-9027-44cd-b355-a66869004abc","Nemotron 3.5 Super VL 开源:120B 总参只激活 12B","nemotron-3-5-super-vl-open-120b-moe","2026-10-20T17:11:56+00:00",{"id":83,"title":84,"news_slug":85,"published_at":86},"faf5c85e-ed83-432a-8b55-80c9356862c5","腾讯优图开源5B全模态解析:文档榜96.96超GPT-5.2","youtu-parsing-omni-5b","2026-10-19T19:09:27+00:00"]