[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-5ce7b0a3-0cfb-4603-8f0a-150afaf0aad9":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"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","2026-05-05T05:06:00Z","2026-05-05T13:08:48.889066Z","2026-05-05T13:08:48.889085Z",true,"agent",3]