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3.5-Plus实践）",{"id":245,"data":246,"type":254},"qwen35p-features-list",{"items":247,"style":253},[248,249,250,251,252],"\u003Cb>100万上下文\u003C\u002Fb>：实际意味着你可以推送大型代码库片段、日志、规范和长对话，无需频繁“分块”。","\u003Cb>自适应工具使用\u003C\u002Fb>：模型经过训练，可自行决定何时调用工具（搜索、代码执行、浏览器、函数），而不是所有操作都在“头脑中”完成。","\u003Cb>多模态 + “视觉智能体”\u003C\u002Fb>：理解图像\u002F文档，并针对桌面\u002F移动应用程序进行操作（能够“点击”和执行步骤的智能体）。","\u003Cb>效率（MoE \u002F 架构）\u003C\u002Fb>：专注于更高的吞吐量和更低的成本；阿里巴巴在公开场合强调显著降低的成本和更好的工作负载扩展性。","\u003Cb>开放生态系统\u003C\u002Fb>：该系列提供开源权重版本和工具（仓库、HF格式），而Plus通常作为托管模型提供，用于生产环境的延迟和稳定性。","unordered","list",{"id":256,"data":257,"type":258},"qwen35p-delim1",{},"delimiter",{"id":260,"data":261,"type":42},"qwen35p-howto",{"text":262,"level":228},"如何快速尝试（无需复杂理论）",{"id":264,"data":265,"type":223},"qwen35p-howto-p",{"text":266},"最快的途径是通过已经托管Qwen 3.5-Plus的提供商（例如网关\u002F聚合器或云工作室）。如果你已有使用“聊天补全”风格API的应用，迁移通常只需更改模型名称并检查上下文和工具限制。",{"id":268,"data":269,"type":272},"qwen35p-code1",{"code":270,"language":271},"\u002F\u002F 最小示例（伪代码）：根据提供商替换端点\u002FSDK\nimport OpenAI from \"openai\";\n\nconst client = new OpenAI({ apiKey: process.env.API_KEY, baseURL: process.env.BASE_URL });\n\nconst res = await client.chat.completions.create({\n  model: \"qwen3.5-plus\",\n  messages: [\n    { role: \"system\", content: \"你是一个完成任务的智能体。\" },\n    { role: \"user\", content: \"检查这个仓库并建议5个安全改进。\" }\n  ]\n});\n\nconsole.log(res.choices[0].message.content);","javascript","code",{"id":274,"data":275,"type":42},"qwen35p-usecases",{"text":276,"level":228},"Qwen 3.5-Plus具有实际优势的用例",{"id":278,"data":279,"type":254},"qwen35p-usecases-list",{"items":280,"style":286},[281,282,283,284,285],"\u003Cb>大型语料库上的智能体RAG\u003C\u002Fb>：100万上下文 + 工具使用减少了对激进摘要的需求。","\u003Cb>仓库级编码\u003C\u002Fb>：分析多个文件 + 生成PR，具有一致的规划（减少“随机拼凑”）。","\u003Cb>UI\u002FQA自动化\u003C\u002Fb>：多模态输入 + “视觉智能体”方向，用于端到端测试和基于录屏\u002F截图的错误复现。","\u003Cb>运维\u002F事件分析\u003C\u002Fb>：大量日志 + 使用工具（搜索、查询、工单）执行运行手册。","\u003Cb>数据工作流智能体\u003C\u002Fb>：生成SQL、验证结果、迭代修复——全部在单个会话中完成，无需丢失上下文。","ordered",{"id":288,"data":289,"type":42},"qwen35p-tradeoffs",{"text":290,"level":228},"权衡（避免陷入炒作陷阱）",{"id":292,"data":293,"type":254},"qwen35p-tradeoffs-list",{"items":294,"style":253},[295,296,297],"\u003Cb>100万上下文 ≠ 100万“完美记忆”\u003C\u002Fb>：输入越大，越需要注意结构（分段、索引、检索计划）。","\u003Cb>智能体行为需要防护措施\u003C\u002Fb>：务必添加策略层（允许的工具、速率限制、沙箱）、日志记录和重放。","\u003Cb>托管 vs 开源权重\u003C\u002Fb>：Plus作为托管模型在延迟\u002F稳定性方面表现优异，但开源权重版本在隐私和本地控制方面更优——同时带来更大的运维负担。",{"id":299,"data":300,"type":223},"qwen35p-bottomline",{"text":301},"\u003Cb>总结：\u003C\u002Fb>Qwen 3.5-Plus是一个信号，表明竞争正从“谁在聊天中更聪明”转向“谁能更可靠地执行复杂工作流”。如果你在生产环境中开发智能体，这值得测试——尤其是当你的瓶颈在于上下文、工具使用和多步骤稳定性时。","2.31","探索阿里巴巴Qwen 3.5-Plus的革命性特性与优势，这款为开发者打造的颠覆性开源人工智能模型。","\u002Fuploads\u002F2026\u002F02\u002Fnew-qwen-3-5-plus-1771515512741-dcbi9p.webp","new-qwen-3-5-plus-1771515512741-dcbi9p","PUBLISHED","2026-02-19T10:23:00.000Z","2026-02-19T15:23:23.973Z","2026-02-20T20:39:04.434Z",{"en":311,"de":312,"sr":313,"es":314,"fr":315,"it":316,"ru":317,"zh":318},"\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Fde\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Fsr\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Fes\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Ffr\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Fit\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Fru\u002Fblog\u002Fnew-qwen-3-5-plus","\u002Fzh\u002Fblog\u002Fnew-qwen-3-5-plus",[320,324,328,332],{"id":321,"name":322,"slug":323},94,"发布运行手册","release-runbook",{"id":325,"name":326,"slug":327},106,"AI回滚运行手册","ai-rollback",{"id":329,"name":330,"slug":331},88,"版本管理（提示\u002F模型）","versioning",{"id":333,"name":334,"slug":335},87,"LLMOps作战手册","llmops",{"id":337,"login":338,"email":339,"displayName":340},"20","rooth8233","aleksandar@stajic.de","Aleksandar Stajić",[342,411],{"lang":343,"title":344,"content":345,"contentJson":346,"excerpt":410},"en","New Qwen 3.5-Plus: Open-source AI is getting serious now","{\"time\":1771533358356,\"blocks\":[{\"id\":\"qwen35p-hero\",\"data\":{\"text\":\"Qwen 3.5-Plus: Open-source \\\"agentic\\\" AI that removes friction for developers in complex tasks\",\"level\":2},\"type\":\"header\"},{\"id\":\"qwen35p-lead\",\"data\":{\"text\":\"Alibaba has released \u003Cb>Qwen 3.5\u003C\u002Fb> and particularly highlights \u003Cb>Qwen 3.5-Plus\u003C\u002Fb> as a model developed for \u003Ci>agentic\u003C\u002Fi> work: planning, tool use, and execution of multi-step tasks with significantly higher efficiency. The message is clear: less \\\"prompt magic\\\", more reliable execution – and all with a context of up to \u003Cb>1 million tokens\u003C\u002Fb> in the Plus variant.\"},\"type\":\"paragraph\"},{\"id\":\"qwen35p-why\",\"data\":{\"text\":\"Why this is important for developers\",\"level\":3},\"type\":\"header\"},{\"id\":\"qwen35p-why-p\",\"data\":{\"text\":\"When you build production agents (RAG, copilots, automatic code review bots, data pipelines, UI testers), the biggest problem isn't \\\"whether the model knows something\\\", but: \u003Cb>can it consistently process the workflow\u003C\u002Fb> without falling apart at the 6th step. Qwen 3.5-Plus targets exactly this area – with large context, multimodal input, and integrated tool-use behavior.\"},\"type\":\"paragraph\"},{\"id\":\"qwen35p-quote\",\"data\":{\"text\":\"This is a model that attempts to transform LLMs from a \\\"chat UI\\\" into an \u003Cb>execution layer\u003C\u002Fb>: it sees, plans, uses tools, and completes the task.\",\"caption\":\"How Qwen 3.5 positions the \\\"agentic\\\" direction\",\"alignment\":\"left\"},\"type\":\"quote\"},{\"id\":\"qwen35p-features\",\"data\":{\"text\":\"Key innovations (Qwen 3.5-Plus in practice)\",\"level\":3},\"type\":\"header\"},{\"id\":\"qwen35p-features-list\",\"data\":{\"items\":[\"\u003Cb>1M Context\u003C\u002Fb>: In practice, this means you can process large codebase snippets, logs, specifications, and long conversations without constant \\\"chunking\\\".\",\"\u003Cb>Adaptive Tool Use\u003C\u002Fb>: The model is trained to decide for itself when a tool (search, code execution, browser, functions) should be called, instead of doing everything \\\"in its head\\\".\",\"\u003Cb>Multimodal + \\\"Visual Agent\\\"\u003C\u002Fb>: Understands images\u002Fdocuments and aims at working across desktop\u002Fmobile apps (an agent that can \\\"click\\\" and execute steps).\",\"\u003Cb>Efficiency (MoE \u002F Architecture)\u003C\u002Fb>: Focus on higher throughput and lower costs; Alibaba emphasizes significantly lower costs and better scaling of workloads in public appearances.\",\"\u003Cb>Open Ecosystem\u003C\u002Fb>: The series includes open-weight editions and tooling (repo, HF formats), while Plus is often offered as a hosted model for production latency and stability.\"],\"style\":\"unordered\"},\"type\":\"list\"},{\"id\":\"qwen35p-delim1\",\"data\":{},\"type\":\"delimiter\"},{\"id\":\"qwen35p-howto\",\"data\":{\"text\":\"How to try it quickly (without further ado)\",\"level\":3},\"type\":\"header\"},{\"id\":\"qwen35p-howto-p\",\"data\":{\"text\":\"The fastest way is through a provider that already hosts Qwen 3.5-Plus (e.g., gateway\u002Faggregator or cloud studio). If you already have an app that uses a \\\"Chat Completions\\\" style API, migration usually consists only of changing the model name and checking context limits and tools.\"},\"type\":\"paragraph\"},{\"id\":\"qwen35p-code1\",\"data\":{\"code\":\"\u002F\u002F Minimal example (pseudo): replace endpoint\u002FSDK depending on provider\\nimport OpenAI from \\\"openai\\\";\\n\\nconst client = new OpenAI({ apiKey: process.env.API_KEY, baseURL: process.env.BASE_URL });\\n\\nconst res = await client.chat.completions.create({\\n  model: \\\"qwen3.5-plus\\\",\\n  messages: [\\n    { role: \\\"system\\\", content: \\\"You are an agent that completes tasks.\\\" },\\n    { role: \\\"user\\\", content: \\\"Go through this repo and suggest 5 security improvements.\\\" }\\n  ]\\n});\\n\\nconsole.log(res.choices[0].message.content);\",\"language\":\"javascript\"},\"type\":\"code\"},{\"id\":\"qwen35p-usecases\",\"data\":{\"text\":\"Use cases where Qwen 3.5-Plus offers a real advantage\",\"level\":3},\"type\":\"header\"},{\"id\":\"qwen35p-usecases-list\",\"data\":{\"items\":[\"\u003Cb>Agentic RAG for large corpora\u003C\u002Fb>: 1M context + tool-use reduces the need for aggressive summarization.\",\"\u003Cb>Repo-Level Coding\u003C\u002Fb>: Analysis of multiple files + generation of PRs with a consistent plan (less \\\"random patchwork\\\").\",\"\u003Cb>UI\u002FQA Automation\u003C\u002Fb>: Multimodal input + \\\"Visual Agent\\\" alignment for end-to-end tests and reproducing bugs from screencasts\u002Fscreenshots.\",\"\u003Cb>Ops\u002FIncident Analysis\u003C\u002Fb>: Large amounts of logs + runbook execution with tools (search, query, ticketing).\",\"\u003Cb>Data Workflow Agent\u003C\u002Fb>: SQL generation, result validation, iterative corrections – all in one session without context loss.\"],\"style\":\"ordered\"},\"type\":\"list\"},{\"id\":\"qwen35p-tradeoffs\",\"data\":{\"text\":\"Trade-offs (to avoid falling into the hype trap)\",\"level\":3},\"type\":\"header\"},{\"id\":\"qwen35p-tradeoffs-list\",\"data\":{\"items\":[\"\u003Cb>1M Context ≠ 1M \\\"perfect memory\\\"\u003C\u002Fb>: The larger the input, the more you need to pay attention to structure (sectioning, index, retrieval plan).\",\"\u003Cb>Agentic behavior requires guardrails\u003C\u002Fb>: Be sure to add a policy layer (allowed tools, rate limit, sandbox), logging, and replay.\",\"\u003Cb>Hosted vs. Open-Weight\u003C\u002Fb>: Plus as a hosted model is excellent for latency\u002Fstability, but open-weight variants are better for data privacy and on-prem control – with higher operational overhead (Ops).\"],\"style\":\"unordered\"},\"type\":\"list\"},{\"id\":\"qwen35p-bottomline\",\"data\":{\"text\":\"\u003Cb>Conclusion:\u003C\u002Fb> Qwen 3.5-Plus is a signal that the race is shifting from \\\"who is smarter in chat\\\" to \\\"who executes complex workflows more reliably\\\". If you use agents in production, this is worth a test – especially if context, tool-use, and stability across multiple steps are your bottlenecks.\"},\"type\":\"paragraph\"}],\"version\":\"2.31\"}",{"time":347,"blocks":348,"version":302},1771533358356,[349,352,355,358,361,365,368,376,378,381,384,387,390,398,401,407],{"id":215,"data":350,"type":42},{"text":351,"level":218},"Qwen 3.5-Plus: Open-source \"agentic\" AI that removes friction for developers in complex tasks",{"id":220,"data":353,"type":223},{"text":354},"Alibaba has released \u003Cb>Qwen 3.5\u003C\u002Fb> and particularly highlights \u003Cb>Qwen 3.5-Plus\u003C\u002Fb> as a model developed for \u003Ci>agentic\u003C\u002Fi> work: planning, tool use, and execution of multi-step tasks with significantly higher efficiency. The message is clear: less \"prompt magic\", more reliable execution – and all with a context of up to \u003Cb>1 million tokens\u003C\u002Fb> in the Plus variant.",{"id":225,"data":356,"type":42},{"text":357,"level":228},"Why this is important for developers",{"id":230,"data":359,"type":223},{"text":360},"When you build production agents (RAG, copilots, automatic code review bots, data pipelines, UI testers), the biggest problem isn't \"whether the model knows something\", but: \u003Cb>can it consistently process the workflow\u003C\u002Fb> without falling apart at the 6th step. Qwen 3.5-Plus targets exactly this area – with large context, multimodal input, and integrated tool-use behavior.",{"id":234,"data":362,"type":239},{"text":363,"caption":364,"alignment":238},"This is a model that attempts to transform LLMs from a \"chat UI\" into an \u003Cb>execution layer\u003C\u002Fb>: it sees, plans, uses tools, and completes the task.","How Qwen 3.5 positions the \"agentic\" direction",{"id":241,"data":366,"type":42},{"text":367,"level":228},"Key innovations (Qwen 3.5-Plus in practice)",{"id":245,"data":369,"type":254},{"items":370,"style":253},[371,372,373,374,375],"\u003Cb>1M Context\u003C\u002Fb>: In practice, this means you can process large codebase snippets, logs, specifications, and long conversations without constant \"chunking\".","\u003Cb>Adaptive Tool Use\u003C\u002Fb>: The model is trained to decide for itself when a tool (search, code execution, browser, functions) should be called, instead of doing everything \"in its head\".","\u003Cb>Multimodal + \"Visual Agent\"\u003C\u002Fb>: Understands images\u002Fdocuments and aims at working across desktop\u002Fmobile apps (an agent that can \"click\" and execute steps).","\u003Cb>Efficiency (MoE \u002F Architecture)\u003C\u002Fb>: Focus on higher throughput and lower costs; Alibaba emphasizes significantly lower costs and better scaling of workloads in public appearances.","\u003Cb>Open Ecosystem\u003C\u002Fb>: The series includes open-weight editions and tooling (repo, HF formats), while Plus is often offered as a hosted model for production latency and stability.",{"id":256,"data":377,"type":258},{},{"id":260,"data":379,"type":42},{"text":380,"level":228},"How to try it quickly (without further ado)",{"id":264,"data":382,"type":223},{"text":383},"The fastest way is through a provider that already hosts Qwen 3.5-Plus (e.g., gateway\u002Faggregator or cloud studio). If you already have an app that uses a \"Chat Completions\" style API, migration usually consists only of changing the model name and checking context limits and tools.",{"id":268,"data":385,"type":272},{"code":386,"language":271},"\u002F\u002F Minimal example (pseudo): replace endpoint\u002FSDK depending on provider\nimport OpenAI from \"openai\";\n\nconst client = new OpenAI({ apiKey: process.env.API_KEY, baseURL: process.env.BASE_URL });\n\nconst res = await client.chat.completions.create({\n  model: \"qwen3.5-plus\",\n  messages: [\n    { role: \"system\", content: \"You are an agent that completes tasks.\" },\n    { role: \"user\", content: \"Go through this repo and suggest 5 security improvements.\" }\n  ]\n});\n\nconsole.log(res.choices[0].message.content);",{"id":274,"data":388,"type":42},{"text":389,"level":228},"Use cases where Qwen 3.5-Plus offers a real advantage",{"id":278,"data":391,"type":254},{"items":392,"style":286},[393,394,395,396,397],"\u003Cb>Agentic RAG for large corpora\u003C\u002Fb>: 1M context + tool-use reduces the need for aggressive summarization.","\u003Cb>Repo-Level Coding\u003C\u002Fb>: Analysis of multiple files + generation of PRs with a consistent plan (less \"random patchwork\").","\u003Cb>UI\u002FQA Automation\u003C\u002Fb>: Multimodal input + \"Visual Agent\" alignment for end-to-end tests and reproducing bugs from screencasts\u002Fscreenshots.","\u003Cb>Ops\u002FIncident Analysis\u003C\u002Fb>: Large amounts of logs + runbook execution with tools (search, query, ticketing).","\u003Cb>Data Workflow Agent\u003C\u002Fb>: SQL generation, result validation, iterative corrections – all in one session without context loss.",{"id":288,"data":399,"type":42},{"text":400,"level":228},"Trade-offs (to avoid falling into the hype trap)",{"id":292,"data":402,"type":254},{"items":403,"style":253},[404,405,406],"\u003Cb>1M Context ≠ 1M \"perfect memory\"\u003C\u002Fb>: The larger the input, the more you need to pay attention to structure (sectioning, index, retrieval plan).","\u003Cb>Agentic behavior requires guardrails\u003C\u002Fb>: Be sure to add a policy layer (allowed tools, rate limit, sandbox), logging, and replay.","\u003Cb>Hosted vs. Open-Weight\u003C\u002Fb>: Plus as a hosted model is excellent for latency\u002Fstability, but open-weight variants are better for data privacy and on-prem control – with higher operational overhead (Ops).",{"id":299,"data":408,"type":223},{"text":409},"\u003Cb>Conclusion:\u003C\u002Fb> Qwen 3.5-Plus is a signal that the race is shifting from \"who is smarter in chat\" to \"who executes complex workflows more reliably\". If you use agents in production, this is worth a test – especially if context, tool-use, and stability across multiple steps are your bottlenecks.","Discover the groundbreaking features and benefits of Alibaba's Qwen 3.5-Plus, a revolutionary open-source AI for developers.",{"lang":7,"title":208,"content":210,"contentJson":412,"excerpt":303},{"time":212,"blocks":413,"version":302},[414,416,418,420,422,424,426,429,431,433,435,437,439,442,444,447],{"id":215,"data":415,"type":42},{"text":217,"level":218},{"id":220,"data":417,"type":223},{"text":222},{"id":225,"data":419,"type":42},{"text":227,"level":228},{"id":230,"data":421,"type":223},{"text":232},{"id":234,"data":423,"type":239},{"text":236,"caption":237,"alignment":238},{"id":241,"data":425,"type":42},{"text":243,"level":228},{"id":245,"data":427,"type":254},{"items":428,"style":253},[248,249,250,251,252],{"id":256,"data":430,"type":258},{},{"id":260,"data":432,"type":42},{"text":262,"level":228},{"id":264,"data":434,"type":223},{"text":266},{"id":268,"data":436,"type":272},{"code":270,"language":271},{"id":274,"data":438,"type":42},{"text":276,"level":228},{"id":278,"data":440,"type":254},{"items":441,"style":286},[281,282,283,284,285],{"id":288,"data":443,"type":42},{"text":290,"level":228},{"id":292,"data":445,"type":254},{"items":446,"style":253},[295,296,297],{"id":299,"data":448,"type":223},{"text":301},"Post erfolgreich abgerufen",{"items":451,"source":470,"manualIds":471,"manualMatchedIds":472},[452,458,463],{"id":453,"slug":454,"title":455,"excerpt":456,"featuredImage":304,"publishedAt":457},"445","qwen-3-6-in-production-release-runbook-ai-rollback-and-llmops-versioning","Qwen 3.6 生产环境部署：发布手册、AI 回滚与 LLMOps 版本管理","Qwen 3.6 不仅仅是一次模型升级。它同时是一个发布事件、一个回滚场景和一个版本管理问题。本文通过LLMOps规范、提示词与模型可追溯性、受控发布以及基于证据的回滚准备，阐述了在生产环境中应如何处理Qwen 3.6。","2026-05-04T02:49:00.000Z",{"id":459,"slug":460,"title":460,"excerpt":10,"featuredImage":461,"publishedAt":462},"369","git-with-automatic-upload-and-synchronization-to-a-production-server","\u002Fuploads\u002F2024\u002F05\u002Fstep-by-step-guide-illustration-showing-the-process-of-setting-up-Git-with-auto-upload-and-synchronization-to-a-production-server-large.webp","2024-05-28T22:48:00.000Z",{"id":464,"slug":465,"title":466,"excerpt":467,"featuredImage":468,"publishedAt":469},"459","ollama-is-not-the-product-building-production-ready-open-llm-applications","Ollama 并非产品：构建可投入生产的开源大语言模型应用","使用Ollama运行本地模型很简单。但构建一个可用于生产环境的开源大语言模型（Open-LLM）应用则更具挑战性：它需要RAG（检索增强生成）、访问控制、供应商抽象、评估、日志记录、部署规范，以及围绕模型构建受控的应用层。","\u002Fuploads\u002F2026\u002F06\u002Follama-is-not-the-product-building-production-ready-open-llm-applications-1782679361640-h0usqf.webp","2026-06-28T16:39:00.000Z","fallback",[],[]]