[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-glm-5-2-deepseek-v4-qwen-3-6-coding":3,"news-related-4c9f74d4-0252-4e86-8b6e-85d38788eea6":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},"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",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":28},"8995f8b9-3eee-4c1b-bfd1-a3ae2b953960","en","Open coding models compared: GLM-5.2, DeepSeek V4, Qwen3.6","The coding-LLM contest has moved from \"open-source vs. closed-source\" to \"open-source vs. open-source\". In the first half of 2026, Z.ai's GLM-5.2, DeepSeek V4, and Alibaba's Qwen3.6 each released their weights in succession, pushing the \"downloadable frontier\" to a new stage. The deployment threshold differs dramatically. GLM-5.2 is a 753B-total \u002F 40B-active MoE under MIT, requiring a few H100s to serve. DeepSeek V4 Pro goes further, with 1.6T total \u002F 49B active, 1M context plus 384K output (the highest of the three), also MIT, and 1.6T is essentially never self-hosted — everyone goes through its $0.435 \u002F $0.87 API. Qwen3.6-35B-A3B is a totally different picture: 35B total \u002F 3B active, Apache 2.0, only 21GB VRAM after Q4 quantization, fitting on a 24GB consumer card — the only one of the three that runs on an ordinary workstation. Benchmarks have to be read by \"cluster\", not by \"leaderboard\". On SWE-bench Pro, GLM-5.2 reports 62.1 to lead; on Terminal-Bench 2.1, 81.0. DeepSeek V4 Pro-Max sits around 80.6% on SWE-bench Verified, but that's vendor-self-reported so discount accordingly. Qwen3.6-35B-A3B takes SWE-bench Verified at 73.4 — and this number is the one to remember, because it's achieved with 3B active parameters, an unmatched per-watt capability density among open-source models. So which to pick? For the strongest MIT-licensed public-weight score, GLM-5.2. For frontier quality with the lowest single-task cost via API, DeepSeek V4 Pro. For must-be-self-hosted, runs on a 24GB card, and data can't leave the box, Qwen3.6-35B-A3B. But for teams actually running at scale, the more honest mid-2026 answer isn't \"pick one\", it's layered routing: high-frequency lightweight on Qwen3.6-35B-A3B (zero marginal cost), hard problems on DeepSeek V4 Pro (cheap and strong), and GLM-5.2 as the open-weights best-quality fallback. The old \"open vs. closed\" narrative is over — the new question is \"open-source you can actually run\".","glm-5-2-deepseek-v4-qwen-3-6-coding","2026-07-27T06:00:00Z","2026-07-27T04:03:34.241461Z","2026-08-19T02:08:40.142862Z",true,"agent",80,{"items":37},[38,43,48,53,58,63],{"id":39,"title":40,"news_slug":41,"published_at":42},"ff0bc92a-295a-4707-be8d-76115fe9eeee","PerceptionBench 出炉:16 个前沿多模态模型,视觉感知无一及格","moonshot-perceptionbench-atomic-perception","2026-08-26T13:15:00+00:00",{"id":44,"title":45,"news_slug":46,"published_at":47},"0d8fdf45-4585-47c0-9e78-3652e318b156","Apple Intelligence 中国版落地:通义千问接管语言 AI,百度负责视觉搜索","apple-intelligence-china-qwen-baidu-2026","2026-08-25T12:00:00+00:00",{"id":49,"title":50,"news_slug":51,"published_at":52},"f9cf9f03-6aca-4d29-94d3-5c6acfeaf435","匿名模型 OX Alpha 短暂登顶 OpenRouter 编码榜:研究者推测底座指向智谱 GLM-5.x","ox-alpha-stealth-openrouter-glm-5-zhipu","2026-08-24T03:00:00+00:00",{"id":54,"title":55,"news_slug":56,"published_at":57},"1844afb1-3a1c-4acd-9e4c-f5e2792a2018","下载免费不等于商用免费：HF Summer 2026 隐藏的开源前沿许可证分水岭","frontier-license-shift-hf-summer-2026","2026-08-23T12:30:00+00:00",{"id":59,"title":60,"news_slug":61,"published_at":62},"7ba15299-8bee-4039-8bc7-dbb58754b562","SWE-bench Science:最强 Claude Code 修科学代码也不及格,四类失败模式被拆解","swe-bench-science-benchmark","2026-08-21T13:00:00+00:00",{"id":64,"title":65,"news_slug":66,"published_at":67},"5a274662-0c3f-492c-a0e8-a46c5a0be783","别再自己给自己打分了:Co-RL 让模型互相判卷,无标签 RL 追平有监督","co-rl-peer-reward-label-free-rl","2026-08-20T19:10:00+00:00"]