[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-moe-vs-dense-open-source-llm-divergence-2026":3,"news-related-5ce7b0a3-0cfb-4603-8f0a-150afaf0aad9":36},{"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},"5ce7b0a3-0cfb-4603-8f0a-150afaf0aad9","开源大模型架构分化：MoE与Dense的技术路线之争","2026年4-5月，开源大模型迎来了史上最高密度的新品发布期。Meta Llama 4、阿里Qwen 3.6、Google Gemma 4、DeepSeek V4、Mistral Medium 3.5以及月之暗面Kimi K2.6相继登场。在这场发布热潮背后，一条清晰的技术路线分化正在浮现：**MoE（混合专家）架构正在成为主流**。\n\n从架构来看，Llama 4 Scout和Maverick都采用了17B活跃参数的MoE设计，Scout在109B总参数中仅激活16个专家，Maverick则扩展至128个专家、400B总参数。Qwen 3.6-235B的MoE配置激活约22B参数，DeepSeek V4 Pro则以49B活跃参数驱动1.6T总参数规模。三家选择高度一致：用稀疏激活换取参数量的指数级膨胀，同时保持推理成本可控。\n\n相比之下，Google的Gemma 4和Mistral的Medium 3.5选择了Dense（密集）架构。Gemma 4-31B采用31B密集参数设计，Mistral Medium 3.5更是128B纯密集模型，均不使用MoE稀疏激活。这两种选择代表不同的工程哲学：Dense架构在特定任务上具有更强的一致性输出能力，但对于给定的激活参数预算，能访问的总知识容量受限于参数量。\n\nBenchmark数据印证了这一分化。DeepSeek V4 Pro在SWE-Bench Verified上达80.6%，Kimi K2.6为80.2%，两者均为MoE架构。Mistral Medium 3.5以77.6%紧随其后，但密集架构在相同激活规模下的知识容量远低于MoE模型——稀疏激活让相同活跃参数能编码更多专业知识。\n\n当前开源生态已进入精细化发展阶段。MoE阵营以DeepSeek V4、Kimi K2.6、Qwen 3.6为代表，Dense阵营则由Gemma 4和Mistral Medium 3.5担纲。技术路线的分化让开发者面临真正的选择：稀疏激活换取规模优势，还是密集架构保证输出稳定性？这个问题的答案，将取决于具体应用场景的推理预算和任务特征。","https:\u002F\u002Fcodersera.com\u002Fblog\u002Fbest-open-source-llm-2026-llama-4-qwen-3-5-deepseek-v4-gemma-4-mistral\u002F","ecf2f2e8-a813-4271-ac8b-65cee6589aa2",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",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},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"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},"4f5b40eb-088e-4ce9-8a57-15a9f65d4897","en","Open-source LLMs split: MoE versus Dense architecture routes","In April-May 2026, open-source LLMs welcomed the most dense new-release period in history. Meta Llama 4, Alibaba Qwen 3.6, Google Gemma 4, DeepSeek V4, Mistral Medium 3.5, and Moonshot Kimi K2.6 have taken the stage one after another. Behind this release wave, a clear technical-path divergence is emerging: **MoE (Mixture of Experts) architecture is becoming the mainstream.**\n\nArchitecturally, both Llama 4 Scout and Maverick use 17B active parameters MoE design; Scout activates only 16 experts within 109B total parameters, while Maverick extends to 128 experts with 400B total. Qwen 3.6-235B's MoE config activates about 22B parameters, and DeepSeek V4 Pro drives 1.6T total scale with 49B active parameters. The three highly consistent in their choice: use sparse activation to trade for exponential parameter scaling while keeping inference cost controllable.\n\nIn contrast, Google's Gemma 4 and Mistral's Medium 3.5 chose Dense architecture. Gemma 4-31B uses 31B dense parameter design, while Mistral Medium 3.5 is a 128B pure dense model, neither using MoE sparse activation. These two choices represent different engineering philosophies: Dense architecture has stronger consistent-output capability on specific tasks, but for a given active-parameter budget, the total knowledge capacity accessible is limited by the parameter count.\n\nBenchmark data confirms this divergence. DeepSeek V4 Pro hits 80.6% on SWE-Bench Verified, Kimi K2.6 at 80.2%, both MoE architecture. Mistral Medium 3.5 follows at 77.6%, but dense architecture's knowledge capacity at the same active scale is far below MoE models — sparse activation lets the same active parameters encode more specialized knowledge.\n\nThe current open-source ecosystem has entered a refined-development stage. The MoE camp is represented by DeepSeek V4, Kimi K2.6, Qwen 3.6, while the Dense camp is led by Gemma 4 and Mistral Medium 3.5. The technical-path divergence leaves developers facing a real choice: sparse activation for scale advantages, or dense architecture for output stability? The answer to this question will depend on the inference budget and task characteristics of specific application scenarios.","moe-vs-dense-open-source-llm-divergence-2026","2026-05-05T05:06:00Z","2026-05-05T13:08:48.889066Z","2026-08-19T02:08:40.142862Z",true,"agent",162,{"items":37},[38,43,48,53,58,63],{"id":39,"title":40,"news_slug":41,"published_at":42},"5878a668-282c-4b88-b2b8-7eef40b7938c","LFM2.5-2.6B：2.5GB 内存跑本机 Agent 220 tok\u002Fs","lfm2-5-2-6b-on-device-agent","2026-08-11T00:00:00+00:00",{"id":44,"title":45,"news_slug":46,"published_at":47},"06651fbd-69a7-42b7-adac-68fc5db5063e","Soofi S 30B 用 MoE + 混合架构挤进完全开源头名:德国把主权 AI 写进 3.2B 激活参数","soofi-s-30b-sovereign","2026-07-13T20:04:00+00:00",{"id":49,"title":50,"news_slug":51,"published_at":52},"222d6fbf-e9bc-4d63-8481-88ea28fd499c","Sber GigaChat 3.5 Ultra 开源：线性注意力 MoE 把长文本速度拉高 4 倍、模型尺寸砍半","sber-gigachat-3-5-ultra","2026-07-10T18:05:00+00:00",{"id":54,"title":55,"news_slug":56,"published_at":57},"31259851-c64e-47dd-99cf-bcfae698b14f","LFM2.5-Retrievers：Liquid AI 把 LFM「单向」改成「双向 350M」，11 语种检索刷 SOTA","lfm-2-5-retrievers-liquid-350m-bidirectional","2026-06-22T03:30:00+00:00",{"id":59,"title":60,"news_slug":61,"published_at":62},"f89d097b-838b-4e1d-a5f6-dc2e6af67fb6","LFM2.5-8B-A1B 开源：1.5B 激活的 MoE 把「边缘 LLM」的天花板再抬一截","lfm-2-5-8b-a1b-liquid-edge-moe-1-5b","2026-06-12T10:00:00+00:00",{"id":64,"title":65,"news_slug":66,"published_at":67},"d2d262c1-6bf0-4a95-b95f-8896fa226db3","腾讯混元 Hy-MT2 翻译家族开源：33 语言 + 1.25-bit 量化","tencent-hy-mt2-33-lang-1-25-bit-440mb","2026-05-22T02:00:00+00:00"]