[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-6f1f105b-8e80-4b2c-b88c-b392556952aa":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":26,"created_at":27,"modified_at":28,"is_published":29,"publish_type":30,"image_url":13,"view_count":31},"6f1f105b-8e80-4b2c-b88c-b392556952aa","2026年本地LLM深度评测：开源模型性能全解析","在2026年的AI领域，开源模型与闭源模型之间的界限已经变得模糊。开发者不再纠结于开源是否够用，而是开始关注哪款开源模型最适合我的特定任务。根据最新基准测试数据，主流本地LLM在三大硬核赛道上展开激烈竞争：SWE-bench Verified（真实软件工程能力）、AIME 2025（竞赛级数学推理）以及τ²-Bench（Agent代理协作能力）。代码能力方面，Kimi K2.5在SWE-bench Verified测试中取得76.8%的惊人成绩，成为开源界的新巅峰。其1万亿参数的MoE架构和256K超长上下文长度，让它在复杂代码处理上表现优异。DeepSeek V3.2凭借完全开放的MIT协议和73.1%的SWE-bench评分，依然是开发者的首选，提供极佳的性价比和响应速度。这场评测表明，开源模型正在迅速逼近闭源模型的性能水平，开发者现在拥有了更多元化、更专业化的模型选择。未来，随着MoE架构和长上下文技术的成熟，本地LLM将在企业级应用中扮演更加重要的角色。","https:\u002F\u002Fexplore.n1n.ai\u002Fzh\u002Fblog\u002F2026-nian-bendi-llm-shendu-pingce-2026-02-14","45954297-59b3-4c1e-a2ef-d14a2511b225",[10,14,17,20,23],{"id":11,"name":12,"slug":12,"description":13,"color":13},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"120fa59a-ff6f-4537-9bf5-f818df636a0e","benchmark",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"id":24,"name":25,"slug":25,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-04-25T11:15:00Z","2026-04-25T19:13:44.177302Z","2026-04-25T19:13:44.177317Z",true,"agent",6]