[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-qwen3-7-max-anthropic-api-claude-code":3,"news-related-37aa0bc9-d135-444f-842e-0b40388d29e9":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},"37aa0bc9-d135-444f-842e-0b40388d29e9","Qwen3.7-Max 原生兼容 Anthropic API 协议：Claude Code 现已可直接调用阿里模型","上个月，业界还在讨论不同模型生态之间的壁垒有多深。短短几周后，这个壁垒正在被打破。\n\n5月19日，阿里在杭州云峰会上发布了 Qwen3.7-Max，随即一个技术细节引发开发者社区热议：该模型原生支持 Anthropic 的 Messages API 协议。这意味着开发者无需任何适配层，就可以用 Claude Code 的界面和工具链，直接调用 Qwen3.7-Max 作为后端。\n\n这在 AI 行业尚属首次。\n\n**从「生态锁定」到「协议互通」**\n\n过去一年，大型语言模型厂商各自为阵：OpenAI 有自己的工具调用格式，Anthropic 有独立的 Agent 协议，Google 和阿里也各自维护封闭接口。开发者想把某个模型接入 Claude Code（或类似工具），通常需要写一层转接适配，工作量不小。\n\nQwen3.7-Max 选择从底层兼容 Anthropic 协议，本质上是把「谁的工具只能谁用」变成了「只要协议对齐，谁都能用」。这不是简单的功能叠加，而是对接口标准化的一次实质性推动。\n\n**对开发者的实际意义**\n\n对于已经在使用 Claude Code 的团队，这意味着多了一个性能接近前沿、定价更低的备选模型——Qwen3.7-Max 输入费用为 .50\u002F百万 token，仅为 Claude Opus 4.7 的约六分之一，但Arena AI Elo 评分达 1475，属于第一梯队的水准。\n\n同时，1M token 的上下文窗口使得用 Qwen 处理大型代码仓库、整本技术文档成为可能，结合 Claude Code 的工具链，实际工作流会更有弹性。\n\n**竞争逻辑：生态比模型更重要**\n\n阿里这一动作背后有清晰的竞争逻辑。在模型性能逐渐逼近的情况下，谁的生态更开放、谁能吸引更多现有工具链的开发者，谁就更有机会抢占市场份额。\n\nAnthropic 显然乐见其成——协议被更多厂商采用，Claude Code 的适用范围也随之扩大，形成双赢。这也在客观上对 OpenAI 的生态封闭策略形成了压力。\n\n当然，跨生态兼容也带来新的问题：不同模型对同一协议的实现深度存在差异，当工具链复杂度上升，调试和兼容性维护的成本会随之增加。这是采用跨 harness 方案前需要评估的实际问题。\n\n总体来看，Qwen3.7-Max 的协议兼容不是噱头，而是大厂竞争中「生态开放」趋势的一个缩影。2026 年的 Agent 市场，正在从「谁家模型最强」转向「谁家生态最通」。","https:\u002F\u002Fventurebeat.com\u002Ftechnology\u002Falibabas-proprietary-qwen3-7-max-can-run-for-35-hours-autonomously-and-supports-external-harnesses-like-anthropics-claude-code","17ff6400-4413-4b16-86fb-99951dbbd08d",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"e82b2d09-81b2-43d1-977e-e018443b3c14","coding-agent",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",{"id":18,"name":19,"slug":19,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"b7989818-f0c6-4cfa-bb7b-8ce92b6bd8f3","en","Qwen3.7-Max speaks the Anthropic API, works in Claude Code","VentureBeat reports that Qwen3.7-Max is natively compatible with the Anthropic API protocol, meaning Claude Code (and other Anthropic-compatible tools) can directly call Qwen3.7-Max. This interoperability move significantly lowers the switching cost for Claude Code users, with Alibaba's closed-source model as a viable alternative.","qwen3-7-max-anthropic-api-claude-code","2026-05-27T10:05:00Z","2026-05-27T10:06:36.774851Z","2026-08-19T02:08:40.142862Z",true,"agent",201,{"items":37},[38,43,48,53,58,63],{"id":39,"title":40,"news_slug":41,"published_at":42},"4bbc55d2-cabc-477f-a3ad-4e2c119aff2a","TokTier 抓住 Agent 推理的隐藏瓶颈：缓存命中 94.1%，分词仍吃掉 64% 首 token 时间","toktier-stateful-tokenization-agent-serving","2026-07-31T17:56:30+00:00",{"id":44,"title":45,"news_slug":46,"published_at":47},"6b52b4a9-d567-46b8-99c1-e9c65ba59b16","SWE-Pruner Pro:ByteDance 让 Agent 自己当剪枝器,省 39% token 还涨分","swe-pruner-pro-bytedance","2026-07-25T12:00:00+00:00",{"id":49,"title":50,"news_slug":51,"published_at":52},"57b691cc-476c-4427-8618-e29127654b34","AMD ROCm 7 原生支持 Qwen3-Coder-Next：单卡 256k 上下文打破推理硬件垄断","amd-rocm7-qwen3-coder-next-256k-mono","2026-05-25T16:10:00+00:00",{"id":54,"title":55,"news_slug":56,"published_at":57},"0d0e5ce8-fa18-4907-b811-2918ff8464e4","FlexSQL：小型LLM如何在Text-to-SQL任务上超越GPT-o3和DeepSeek-R1","flexsql-nus-text-to-sql-spider2-65pct-gpt-oss-120b","2026-05-05T10:15:00+00:00",{"id":59,"title":60,"news_slug":61,"published_at":62},"69e52a42-19c7-4580-8c49-5446233fbdde","7B模型如何超越GPT-4o？ICLR Oral论文揭示AgentFlow流式训练新范式","agentflow-7b-icrl-oral-flow-grpo-14-9pct","2026-05-03T01:10:00+00:00",{"id":64,"title":65,"news_slug":66,"published_at":67},"cac485ab-a429-4ebc-88c6-a1f924f978ff","AWS 开源 KeysAndValues:微调时就让模型学会“遗忘”,单张 A100 撑住 128K","aws-keysvalues-sparse-attention-finetuning","2026-08-26T05:20:00+00:00"]