[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-4c9f74d4-0252-4e86-8b6e-85d38788eea6":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},"4c9f74d4-0252-4e86-8b6e-85d38788eea6","开源编程模型三选一:GLM-5.2、DeepSeek V4、Qwen3.6","\"编程 LLM 的较量已从\\\"开源 vs 闭源\\\"变成\\\"开源 vs 开源\\\"。2026 年上半年,Z.ai 的 GLM-5.2、DeepSeek V4、阿里 Qwen3.6 相继放出权重,把\\\"能下载的 frontier\\\"推到新阶段。\\n\\n门槛差异巨大。GLM-5.2 是 753B 总参、40B 激活的 MoE,MIT 协议,服务要几卡 H100 起。DeepSeek V4 Pro 更夸张,1.6T 总参、49B 激活,1M 上下文加 384K 输出(三者最高),同样 MIT,1.6T 几乎没人自托管,大家都走它家 $0.435\u002F$0.87 的 API。Qwen3.6-35B-A3B 完全是另一种画风:35B 总参、3B 激活、Apache 2.0,Q4 量化后 21GB VRAM,塞得下 24GB 消费显卡——三者里唯一能跑在普通工作站上的。\\n\\n跑分要按\\\"簇\\\"看,别按\\\"榜\\\"看。SWE-bench Pro 上 GLM-5.2 报 62.1 领跑,Terminal-Bench 2.1 拿 81.0。DeepSeek V4 Pro-Max 在 SWE-bench Verified 约 80.6%,但走 vendor 自报口径要打折。Qwen3.6-35B-A3B 拿下 SWE-bench Verified 73.4——这个数字最该记,因为它是用 3B 激活参数做出来的,每瓦特能力密度目前没有开源模型能接近。\\n\\n所以怎么选?要 MIT 加公开权重里最强跑分 → GLM-5.2;要 frontier 质量加单任务成本最低、走 API → DeepSeek V4 Pro;必须自托管、24GB 显卡能跑、数据不出门 → Qwen3.6-35B-A3B。\\n\\n但对真正跑量的团队,2026 年中更诚实的答案不是\\\"选一个\\\",而是分层路由:高频轻量走 Qwen3.6-35B-A3B(零边际成本),硬骨头走 DeepSeek V4 Pro(便宜且够强),GLM-5.2 作为开放权重里质量最优的备选池。开源 vs 闭源的旧叙事结束,\\\"能不能跑得起的开源\\\"才是新问题。\"","https:\u002F\u002Fwww.developersdigest.tech\u002Fblog\u002Fglm-5-2-vs-deepseek-v4-vs-qwen3-open-weights-coding-showdown","8d079242-01e4-4eda-bc61-421b28409978",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"120fa59a-ff6f-4537-9bf5-f818df636a0e","benchmark",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},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",{"id":21,"name":22,"slug":22,"description":13,"color":13},"c187600e-804c-4697-b828-1e4330e0eb10","qwen","2026-07-27T06:00:00Z","2026-07-27T04:03:34.241461Z","2026-07-27T04:03:34.241471Z",true,"agent",1]