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Die Botschaft ist klar: weniger „Prompt-Magie“, mehr zuverlässige Ausführung – und das mit einem Kontext von bis zu \u003Cb>1 Mio. Token\u003C\u002Fb> in der Plus-Variante.\u003C\u002Fp>\n\u003Ch3>Warum dies für Entwickler wichtig ist\u003C\u002Fh3>\n\u003Cp>Wenn Sie Produktionsagenten erstellen (RAG, Copiloten, automatische Code-Review-Bots, Daten-Pipelines, UI-Tester), ist das größte Problem nicht, „ob das Modell etwas weiß“, sondern: \u003Cb>kann es den Workflow konsistent abarbeiten\u003C\u002Fb>, ohne im 6. Schritt auseinanderzufallen. Qwen 3.5-Plus zielt genau auf diesen Bereich ab – und das mit großem Kontext, multimodalem Input und integriertem Tool-Use-Verhalten.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Dies ist ein Modell, das versucht, LLMs von einem „Chat-UI“ in eine \u003Cb>Ausführungsebene\u003C\u002Fb> zu verwandeln: Es sieht, plant, nutzt Werkzeuge und schließt die Aufgabe ab.\u003Ccite class=\"block mt-2 text-sm\">— Wie Qwen 3.5 die „agentische“ Richtung positioniert\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Ch3>Wichtigste Neuerungen (Qwen 3.5-Plus in der Praxis)\u003C\u002Fh3>\n\u003Cul>\u003Cli>\u003Cb>1 Mio. Kontext\u003C\u002Fb>: Das bedeutet in der Praxis, dass Sie große Codebase-Ausschnitte, Logs, Spezifikationen und lange Gespräche ohne ständiges „Chunking“ verarbeiten können.\u003C\u002Fli>\u003Cli>\u003Cb>Adaptive Tool Use\u003C\u002Fb>: Das Modell ist darauf trainiert, selbst zu entscheiden, wann ein Werkzeug (Suche, Code-Ausführung, Browser, Funktionen) aufgerufen werden soll, anstatt alles „im Kopf“ zu erledigen.\u003C\u002Fli>\u003Cli>\u003Cb>Multimodal + „Visual Agent“\u003C\u002Fb>: Versteht Bilder\u002FDokumente und zielt auf die Arbeit über Desktop-\u002FMobile-Apps ab (ein Agent, der „klicken“ und Schritte ausführen kann).\u003C\u002Fli>\u003Cli>\u003Cb>Effizienz (MoE \u002F Architektur)\u003C\u002Fb>: Fokus auf höheren Durchsatz und geringere Kosten; Alibaba betont in öffentlichen Auftritten deutlich niedrigere Kosten und eine bessere Skalierung von Workloads.\u003C\u002Fli>\u003Cli>\u003Cb>Offenes Ökosystem\u003C\u002Fb>: Die Serie umfasst Open-Weight-Editionen und Tooling (Repo, HF-Formate), während Plus oft als gehostetes Modell für Produktionslatenz und Stabilität angeboten wird.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch3>Wie man es schnell ausprobiert (ohne Umschweife)\u003C\u002Fh3>\n\u003Cp>Der schnellste Weg führt über einen Provider, der Qwen 3.5-Plus bereits hostet (z. 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Wenn Sie bereits eine App haben, die eine API im „Chat Completions“-Stil nutzt, besteht die Migration meist nur aus der Änderung des Modellnamens sowie der Überprüfung der Kontextlimits und Werkzeuge.\u003C\u002Fp>\n\u003Cpre class=\"code-block\" data-lang=\"javascript\">\u003Ccode class=\"language-javascript\">\u002F\u002F Minimalbeispiel (Pseudo): Endpunkt\u002FSDK je nach Provider ersetzen\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: "Du bist ein Agent, der Aufgaben erledigt." },\n    { role: "user", content: "Gehe durch dieses Repo und schlage 5 Sicherheitsverbesserungen vor." }\n  ]\n});\n\nconsole.log(res.choices[0].message.content);\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Ch3>Anwendungsfälle, in denen Qwen 3.5-Plus einen echten Vorteil bietet\u003C\u002Fh3>\n\u003Col>\u003Cli>\u003Cb>Agentisches RAG bei großen Korpora\u003C\u002Fb>: 1 Mio. 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Kontext ≠ 1 Mio. „perfektes Gedächtnis“\u003C\u002Fb>: Je größer der Input, desto mehr müssen Sie auf die Struktur achten (Sektionierung, Index, Retrieval-Plan).\u003C\u002Fli>\u003Cli>\u003Cb>Agentisches Verhalten erfordert Guardrails\u003C\u002Fb>: Fügen Sie unbedingt eine Richtlinienebene (erlaubte Werkzeuge, Rate-Limit, Sandbox), Protokollierung und Replay hinzu.\u003C\u002Fli>\u003Cli>\u003Cb>Hosted vs. Open-Weight\u003C\u002Fb>: Plus als gehostetes Modell ist hervorragend für Latenz\u002FStabilität, aber Open-Weight-Varianten sind besser für Datenschutz und On-Prem-Kontrolle – bei höherem Betriebsaufwand (Ops).\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>\u003Cb>Fazit:\u003C\u002Fb> Qwen 3.5-Plus ist ein Signal dafür, dass sich das Rennen von „wer ist im Chat klüger“ hin zu „wer führt komplexe Workflows zuverlässiger aus“ verschiebt. Wenn Sie Agenten in der Produktion einsetzen, ist dies einen Test wert – besonders wenn Kontext, Tool-Use und Stabilität über mehrere Schritte hinweg Ihre Engpässe sind.\u003C\u002Fp>",{"time":227,"blocks":228,"version":317},1771515588814,[229,234,239,244,248,255,259,270,274,278,282,288,292,302,306,313],{"id":230,"data":231,"type":41},"qwen35p-hero",{"text":232,"level":233},"Qwen 3.5-Plus: Open-Source „agentische“ KI, die Entwicklern die Reibung bei komplexen Aufgaben nimmt",2,{"id":235,"data":236,"type":238},"qwen35p-lead",{"text":237},"Alibaba hat \u003Cb>Qwen 3.5\u003C\u002Fb> veröffentlicht und hebt besonders \u003Cb>Qwen 3.5-Plus\u003C\u002Fb> als ein Modell hervor, das für \u003Ci>agentisches\u003C\u002Fi> Arbeiten entwickelt wurde: Planung, Werkzeugnutzung und Ausführung mehrstufiger Aufgaben mit deutlich höherer Effizienz. Die Botschaft ist klar: weniger „Prompt-Magie“, mehr zuverlässige Ausführung – und das mit einem Kontext von bis zu \u003Cb>1 Mio. 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Wenn Sie bereits eine App haben, die eine API im „Chat Completions“-Stil nutzt, besteht die Migration meist nur aus der Änderung des Modellnamens sowie der Überprüfung der Kontextlimits und Werkzeuge.",{"id":283,"data":284,"type":287},"qwen35p-code1",{"code":285,"language":286},"\u002F\u002F Minimalbeispiel (Pseudo): Endpunkt\u002FSDK je nach Provider ersetzen\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: \"Du bist ein Agent, der Aufgaben erledigt.\" },\n    { role: \"user\", content: \"Gehe durch dieses Repo und schlage 5 Sicherheitsverbesserungen vor.\" }\n  ]\n});\n\nconsole.log(res.choices[0].message.content);","javascript","code",{"id":289,"data":290,"type":41},"qwen35p-usecases",{"text":291,"level":243},"Anwendungsfälle, in denen Qwen 3.5-Plus einen echten Vorteil bietet",{"id":293,"data":294,"type":269},"qwen35p-usecases-list",{"items":295,"style":301},[296,297,298,299,300],"\u003Cb>Agentisches RAG bei großen Korpora\u003C\u002Fb>: 1 Mio. Kontext + Tool-Use reduziert die Notwendigkeit für aggressives Zusammenfassen (Summarization).","\u003Cb>Repo-Level Coding\u003C\u002Fb>: Analyse mehrerer Dateien + Generierung von PRs mit einem konsistenten Plan (weniger „zufälliges Patchwork“).","\u003Cb>UI\u002FQA-Automatisierung\u003C\u002Fb>: Multimodaler Input + „Visual Agent“-Ausrichtung für End-to-End-Tests und die Reproduktion von Fehlern aus Screencasts\u002FScreenshots.","\u003Cb>Ops\u002FIncident-Analyse\u003C\u002Fb>: Große Mengen an Logs + Runbook-Ausführung mit Werkzeugen (Suche, Abfrage, Ticketing).","\u003Cb>Daten-Workflow-Agent\u003C\u002Fb>: Generierung von SQL, Validierung von Ergebnissen, iterative Korrekturen – alles in einer Sitzung ohne Kontextverlust.","ordered",{"id":303,"data":304,"type":41},"qwen35p-tradeoffs",{"text":305,"level":243},"Abwägungen (um nicht in die Hype-Falle zu tappen)",{"id":307,"data":308,"type":269},"qwen35p-tradeoffs-list",{"items":309,"style":268},[310,311,312],"\u003Cb>1 Mio. Kontext ≠ 1 Mio. „perfektes Gedächtnis“\u003C\u002Fb>: Je größer der Input, desto mehr müssen Sie auf die Struktur achten (Sektionierung, Index, Retrieval-Plan).","\u003Cb>Agentisches Verhalten erfordert Guardrails\u003C\u002Fb>: Fügen Sie unbedingt eine Richtlinienebene (erlaubte Werkzeuge, Rate-Limit, Sandbox), Protokollierung und Replay hinzu.","\u003Cb>Hosted vs. Open-Weight\u003C\u002Fb>: Plus als gehostetes Modell ist hervorragend für Latenz\u002FStabilität, aber Open-Weight-Varianten sind besser für Datenschutz und On-Prem-Kontrolle – bei höherem Betriebsaufwand (Ops).",{"id":314,"data":315,"type":238},"qwen35p-bottomline",{"text":316},"\u003Cb>Fazit:\u003C\u002Fb> Qwen 3.5-Plus ist ein Signal dafür, dass sich das Rennen von „wer ist im Chat klüger“ hin zu „wer führt komplexe Workflows zuverlässiger aus“ verschiebt. Wenn Sie Agenten in der Produktion einsetzen, ist dies einen Test wert – besonders wenn Kontext, Tool-Use und Stabilität über mehrere Schritte hinweg Ihre Engpässe sind.","2.31","Entdecken Sie die bahnbrechenden Funktionen und Vorteile von Alibabas Qwen 3.5-Plus, einer revolutionären Open-Source-KI für Entwickler.","\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":326,"de":327,"sr":328,"es":329,"fr":330,"it":331,"ru":332,"zh":333},"\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",[335,339,343,347],{"id":336,"name":337,"slug":338},94,"Release-Runbook","release-runbook",{"id":340,"name":341,"slug":342},106,"AI-Rollback-Runbook","ai-rollback",{"id":344,"name":345,"slug":346},88,"Versionierung (Prompts, Modelle)","versioning",{"id":348,"name":349,"slug":350},87,"Playbook: LLMOps","llmops",{"id":352,"login":353,"email":354,"displayName":355},"20","rooth8233","","Aleksandar Stajić",[357,395],{"lang":7,"title":223,"content":225,"contentJson":358,"excerpt":318},{"time":227,"blocks":359,"version":317},[360,362,364,366,368,370,372,375,377,379,381,383,385,388,390,393],{"id":230,"data":361,"type":41},{"text":232,"level":233},{"id":235,"data":363,"type":238},{"text":237},{"id":240,"data":365,"type":41},{"text":242,"level":243},{"id":245,"data":367,"type":238},{"text":247},{"id":249,"data":369,"type":254},{"text":251,"caption":252,"alignment":253},{"id":256,"data":371,"type":41},{"text":258,"level":243},{"id":260,"data":373,"type":269},{"items":374,"style":268},[263,264,265,266,267],{"id":271,"data":376,"type":273},{},{"id":275,"data":378,"type":41},{"text":277,"level":243},{"id":279,"data":380,"type":238},{"text":281},{"id":283,"data":382,"type":287},{"code":285,"language":286},{"id":289,"data":384,"type":41},{"text":291,"level":243},{"id":293,"data":386,"type":269},{"items":387,"style":301},[296,297,298,299,300],{"id":303,"data":389,"type":41},{"text":305,"level":243},{"id":307,"data":391,"type":269},{"items":392,"style":268},[310,311,312],{"id":314,"data":394,"type":238},{"text":316},{"lang":396,"title":397,"content":398,"contentJson":399,"excerpt":463},"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":400,"blocks":401,"version":317},1771533358356,[402,405,408,411,414,418,421,429,431,434,437,440,443,451,454,460],{"id":230,"data":403,"type":41},{"text":404,"level":233},"Qwen 3.5-Plus: Open-source \"agentic\" AI that removes friction for developers in complex tasks",{"id":235,"data":406,"type":238},{"text":407},"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":240,"data":409,"type":41},{"text":410,"level":243},"Why this is important for developers",{"id":245,"data":412,"type":238},{"text":413},"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":249,"data":415,"type":254},{"text":416,"caption":417,"alignment":253},"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":256,"data":419,"type":41},{"text":420,"level":243},"Key innovations (Qwen 3.5-Plus in practice)",{"id":260,"data":422,"type":269},{"items":423,"style":268},[424,425,426,427,428],"\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":271,"data":430,"type":273},{},{"id":275,"data":432,"type":41},{"text":433,"level":243},"How to try it quickly (without further ado)",{"id":279,"data":435,"type":238},{"text":436},"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":283,"data":438,"type":287},{"code":439,"language":286},"\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":289,"data":441,"type":41},{"text":442,"level":243},"Use cases where Qwen 3.5-Plus offers a real advantage",{"id":293,"data":444,"type":269},{"items":445,"style":301},[446,447,448,449,450],"\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":303,"data":452,"type":41},{"text":453,"level":243},"Trade-offs (to avoid falling into the hype trap)",{"id":307,"data":455,"type":269},{"items":456,"style":268},[457,458,459],"\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":314,"data":461,"type":238},{"text":462},"\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.","Post erfolgreich abgerufen",{"items":466,"source":507,"manualIds":508,"manualMatchedIds":509},[467,473,480,486,493,500],{"id":468,"slug":469,"title":470,"excerpt":9,"featuredImage":471,"publishedAt":472},"369","git-with-automatic-upload-and-synchronization-to-a-production-server","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":474,"slug":475,"title":476,"excerpt":477,"featuredImage":478,"publishedAt":479},"496","why-does-ai-retrieve-some-sources-and-ignore-others-the-retrieval-authority-layer","Warum ruft KI einige Quellen ab und ignoriert andere? — Die Retrieval-Autoritätsschicht","Ein relevantes Dokument zu finden, reicht nicht aus. KI-Systeme müssen auch Quellen, die aktuell, maßgeblich, anwendbar und nachvollziehbar sind, von Quellen unterscheiden, die lediglich ähnlich aussehen. Die Retrieval Authority Layer definiert diese Grenze der Quellenauswahl.","\u002Fuploads\u002F2026\u002F10\u002Fwhy-does-ai-retrieve-some-sources-and-ignore-others-the-retrieval-authority-layer-1792072410616-0evp1z.webp","2026-10-15T07:51:00.000Z",{"id":481,"slug":482,"title":483,"excerpt":484,"featuredImage":319,"publishedAt":485},"445","qwen-3-6-in-production-release-runbook-ai-rollback-and-llmops-versioning","Qwen 3.6 in der Produktion: Release-Runbook, KI-Rollback und LLMOps-Versionierung","Qwen 3.6 ist nicht nur ein weiteres Modell-Upgrade. Es ist gleichzeitig ein Release-Ereignis, ein Rollback-Szenario und ein Versionierungsproblem. Dieser Artikel erklärt, wie Qwen 3.6 in der Produktion durch LLMOps-Disziplin, Prompt- und Modell-Rückverfolgbarkeit, kontrollierten Rollout und evidenzbasierte Rollback-Bereitschaft gehandhabt werden sollte.","2026-05-04T02:49:00.000Z",{"id":487,"slug":488,"title":489,"excerpt":490,"featuredImage":491,"publishedAt":492},"493","mlops-vs-llmops-what-changes-when-the-model-is-an-llm","MLOps vs. LLMOps: Was sich ändert, wenn das Modell ein LLM ist","MLOps betreibt Systeme für maschinelles Lernen; LLMOps erweitert diese Praktiken auf Prompts, Kontext, Retrieval, Anbieter, Tools, Evaluierungen und Laufzeitverhalten rund um große Sprachmodelle.","\u002Fuploads\u002F2026\u002F10\u002Fmlops-vs-llmops-what-changes-when-the-model-is-an-llm-1791487319869-2v7hxo.webp","2026-10-08T15:20:00.000Z",{"id":494,"slug":495,"title":496,"excerpt":497,"featuredImage":498,"publishedAt":499},"459","ollama-is-not-the-product-building-production-ready-open-llm-applications","Ollama ist nicht das Produkt: Entwicklung produktionsreifer Open-LLM-Anwendungen","Das Ausführen eines lokalen Modells mit Ollama ist einfach. Das Erstellen einer produktionsreifen Open-LLM-Anwendung ist schwieriger: Es erfordert RAG, Zugriffskontrolle, Anbieterabstraktion, Evaluierung, Protokollierung, Bereitstellungsdisziplin und eine kontrollierte Anwendungsschicht um das Modell herum.","\u002Fuploads\u002F2026\u002F06\u002Follama-is-not-the-product-building-production-ready-open-llm-applications-1782679361640-h0usqf.webp","2026-06-28T16:39:00.000Z",{"id":501,"slug":502,"title":503,"excerpt":504,"featuredImage":505,"publishedAt":506},"488","what-is-context-engineering-what-the-model-receives-before-it-answers","Was ist Context Engineering? Was das Modell erhält, bevor es antwortet","Context Engineering gestaltet, welche Informationen ein KI-Modell vor der Inferenz erhält, einschließlich Prompts, Retrieval, Speicher, Anwendungszustand, Tool-Ergebnissen und Konversationsverlauf.","\u002Fuploads\u002F2026\u002F10\u002Fwhat-is-context-engineering-what-the-model-receives-before-it-answers-1791480653258-018kcv.webp","2026-10-08T13:29:00.000Z","fallback",[],[]]