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3.5-Plus\u003C\u002Fb> como un modelo diseñado para el trabajo \u003Ci>agéntico\u003C\u002Fi>: planificación, uso de herramientas y ejecución de tareas de varios pasos con una eficiencia significativamente mayor. El mensaje es claro: menos “magia de prompts”, más ejecución fiable — y con un contexto que llega hasta \u003Cb>1M de tokens\u003C\u002Fb> en la variante Plus.\u003C\u002Fp>\n\u003Ch3>¿Por qué es esto importante para los desarrolladores?\u003C\u002Fh3>\n\u003Cp>Si estás construyendo agentes de producción (RAG, copilotos, bots automáticos de revisión de código, pipelines de datos, testers de UI), el mayor problema no es si “el modelo sabe algo”, sino: \u003Cb>si puede completar consistentemente un flujo de trabajo\u003C\u002Fb> sin desmoronarse en el sexto paso. Qwen 3.5-Plus apunta precisamente a esa zona — y lo hace con un gran contexto, entrada multimodal y comportamiento de uso de herramientas integrado.\u003C\u002Fp>\n\u003Cblockquote class=\"border-l-4 border-gray-300 pl-4 italic\">Este es un modelo que intenta transformar el LLM de una “interfaz de chat” en una \u003Cb>capa de ejecución\u003C\u002Fb>: ve, planifica, utiliza herramientas y termina el trabajo.\u003Ccite class=\"block mt-2 text-sm\">— Cómo posiciona Qwen 3.5 la dirección “agéntica”\u003C\u002Fcite>\u003C\u002Fblockquote>\n\u003Ch3>Novedades más importantes (Qwen 3.5-Plus en la práctica)\u003C\u002Fh3>\n\u003Cul>\u003Cli>\u003Cb>Contexto de 1M\u003C\u002Fb>: en la práctica significa que puedes introducir grandes fragmentos de código, logs, especificaciones y conversaciones largas sin necesidad de fragmentar constantemente.\u003C\u002Fli>\u003Cli>\u003Cb>Uso adaptativo de herramientas\u003C\u002Fb>: el modelo está entrenado para decidir por sí mismo cuándo llamar a una herramienta (búsqueda, ejecución de código, navegador, funciones) en lugar de hacerlo todo “en su cabeza”.\u003C\u002Fli>\u003Cli>\u003Cb>Multimodal + “agente visual”\u003C\u002Fb>: entiende imágenes\u002Fdocumentos y apunta al trabajo a través de aplicaciones de escritorio\u002Fmóviles (un agente que puede “hacer clic” y ejecutar pasos).\u003C\u002Fli>\u003Cli>\u003Cb>Eficiencia (MoE \u002F arquitectura)\u003C\u002Fb>: enfoque en un mayor rendimiento y menores costes; Alibaba destaca en sus declaraciones públicas un coste significativamente menor y un mejor escalado de las cargas de trabajo.\u003C\u002Fli>\u003Cli>\u003Cb>Ecosistema abierto\u003C\u002Fb>: la serie viene con versiones de pesos abiertos y herramientas (repositorios, formatos HF), mientras que el Plus se ofrece a menudo como un modelo alojado para latencia y estabilidad de producción.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cdiv class=\"ce-delimiter cdx-block my-8\">\u003C\u002Fdiv>\n\u003Ch3>Cómo probarlo rápido (sin rodeos)\u003C\u002Fh3>\n\u003Cp>La forma más rápida es a través de un proveedor que ya aloje Qwen 3.5-Plus (por ejemplo, un gateway\u002Fagregador o un estudio en la nube). Si ya tienes una aplicación que utiliza una API de estilo “chat completions”, la migración consiste principalmente en cambiar el nombre del modelo y verificar los límites de contexto y herramientas.\u003C\u002Fp>\n\u003Cpre class=\"code-block\" data-lang=\"javascript\">\u003Ccode class=\"language-javascript\">\u002F\u002F Ejemplo mínimo (pseudo): reemplaza el endpoint\u002FSDK según el proveedor\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: "Eres un agente que completa tareas." },\n    { role: "user", content: "Revisa este repositorio y sugiere 5 mejoras de seguridad." }\n  ]\n});\n\nconsole.log(res.choices[0].message.content);\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Ch3>Casos de uso donde Qwen 3.5-Plus tiene una ventaja real\u003C\u002Fh3>\n\u003Col>\u003Cli>\u003Cb>RAG agéntico en grandes corpus\u003C\u002Fb>: el contexto de 1M + el uso de herramientas reduce la necesidad de resúmenes agresivos.\u003C\u002Fli>\u003Cli>\u003Cb>Codificación a nivel de repositorio\u003C\u002Fb>: análisis de múltiples archivos + generación de PRs con un plan consistente (menos “parches aleatorios”).\u003C\u002Fli>\u003Cli>\u003Cb>Automatización de UI\u002FQA\u003C\u002Fb>: entrada multimodal + dirección de “agente visual” para pruebas de extremo a extremo y reproducción de errores a partir de grabaciones de pantalla\u002Fcapturas.\u003C\u002Fli>\u003Cli>\u003Cb>Análisis de operaciones\u002Fincidentes\u003C\u002Fb>: grandes volúmenes de logs + ejecución de runbooks con herramientas (búsqueda, consulta, tickets).\u003C\u002Fli>\u003Cli>\u003Cb>Agente de flujo de trabajo de datos\u003C\u002Fb>: generación de SQL, validación de resultados, correcciones iterativas — todo en una sola sesión sin perder el contexto.\u003C\u002Fli>\u003C\u002Fol>\n\u003Ch3>Compromisos (para no caer en la trampa del hype)\u003C\u002Fh3>\n\u003Cul>\u003Cli>\u003Cb>Contexto de 1M ≠ 1M de “memoria perfecta”\u003C\u002Fb>: cuanto mayor sea la entrada, más cuidado debes tener con la estructura (seccionamiento, índice, plan de recuperación).\u003C\u002Fli>\u003Cli>\u003Cb>El comportamiento agéntico requiere barandillas (guardrails)\u003C\u002Fb>: es obligatorio añadir una capa de políticas (herramientas permitidas, límite de velocidad, sandbox), registro y reproducción.\u003C\u002Fli>\u003Cli>\u003Cb>Alojado vs pesos abiertos\u003C\u002Fb>: Plus como modelo alojado es excelente para la latencia\u002Festabilidad, pero las variantes de pesos abiertos son mejores para la privacidad y el control local (on-prem), aunque con una mayor carga operativa.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>\u003Cb>Conclusión:\u003C\u002Fb> Qwen 3.5-Plus es una señal de que la carrera se está desplazando de “quién es más inteligente en el chat” a “quién ejecuta flujos de trabajo complejos de manera más fiable”. Si estás creando agentes en producción, vale la pena probarlo, especialmente cuando el cuello de botella es el contexto, el uso de herramientas y la estabilidad a través de múltiples pasos.\u003C\u002Fp>",{"time":228,"blocks":229,"version":318},1771515707168,[230,235,240,245,249,256,260,271,275,279,283,289,293,303,307,314],{"id":231,"data":232,"type":42},"qwen35p-hero",{"text":233,"level":234},"Qwen 3.5-Plus: IA “agéntica” de código abierto que elimina la fricción de tareas complejas para los desarrolladores",2,{"id":236,"data":237,"type":239},"qwen35p-lead",{"text":238},"Alibaba ha lanzado \u003Cb>Qwen 3.5\u003C\u002Fb> y ha destacado especialmente \u003Cb>Qwen 3.5-Plus\u003C\u002Fb> como un modelo diseñado para el trabajo \u003Ci>agéntico\u003C\u002Fi>: planificación, uso de herramientas y ejecución de tareas de varios pasos con una eficiencia significativamente mayor. El mensaje es claro: menos “magia de prompts”, más ejecución fiable — y con un contexto que llega hasta \u003Cb>1M de tokens\u003C\u002Fb> en la variante Plus.","paragraph",{"id":241,"data":242,"type":42},"qwen35p-why",{"text":243,"level":244},"¿Por qué es esto importante para los desarrolladores?",3,{"id":246,"data":247,"type":239},"qwen35p-why-p",{"text":248},"Si estás construyendo agentes de producción (RAG, copilotos, bots automáticos de revisión de código, pipelines de datos, testers de UI), el mayor problema no es si “el modelo sabe algo”, sino: \u003Cb>si puede completar consistentemente un flujo de trabajo\u003C\u002Fb> sin desmoronarse en el sexto paso. Qwen 3.5-Plus apunta precisamente a esa zona — y lo hace con un gran contexto, entrada multimodal y comportamiento de uso de herramientas integrado.",{"id":250,"data":251,"type":255},"qwen35p-quote",{"text":252,"caption":253,"alignment":254},"Este es un modelo que intenta transformar el LLM de una “interfaz de chat” en una \u003Cb>capa de ejecución\u003C\u002Fb>: ve, planifica, utiliza herramientas y termina el trabajo.","Cómo posiciona Qwen 3.5 la dirección “agéntica”","left","quote",{"id":257,"data":258,"type":42},"qwen35p-features",{"text":259,"level":244},"Novedades más importantes (Qwen 3.5-Plus en la práctica)",{"id":261,"data":262,"type":270},"qwen35p-features-list",{"items":263,"style":269},[264,265,266,267,268],"\u003Cb>Contexto de 1M\u003C\u002Fb>: en la práctica significa que puedes introducir grandes fragmentos de código, logs, especificaciones y conversaciones largas sin necesidad de fragmentar constantemente.","\u003Cb>Uso adaptativo de herramientas\u003C\u002Fb>: el modelo está entrenado para decidir por sí mismo cuándo llamar a una herramienta (búsqueda, ejecución de código, navegador, funciones) en lugar de hacerlo todo “en su cabeza”.","\u003Cb>Multimodal + “agente visual”\u003C\u002Fb>: entiende imágenes\u002Fdocumentos y apunta al trabajo a través de aplicaciones de escritorio\u002Fmóviles (un agente que puede “hacer clic” y ejecutar pasos).","\u003Cb>Eficiencia (MoE \u002F arquitectura)\u003C\u002Fb>: enfoque en un mayor rendimiento y menores costes; Alibaba destaca en sus declaraciones públicas un coste significativamente menor y un mejor escalado de las cargas de trabajo.","\u003Cb>Ecosistema abierto\u003C\u002Fb>: la serie viene con versiones de pesos abiertos y herramientas (repositorios, formatos HF), mientras que el Plus se ofrece a menudo como un modelo alojado para latencia y estabilidad de producción.","unordered","list",{"id":272,"data":273,"type":274},"qwen35p-delim1",{},"delimiter",{"id":276,"data":277,"type":42},"qwen35p-howto",{"text":278,"level":244},"Cómo probarlo rápido (sin rodeos)",{"id":280,"data":281,"type":239},"qwen35p-howto-p",{"text":282},"La forma más rápida es a través de un proveedor que ya aloje Qwen 3.5-Plus (por ejemplo, un gateway\u002Fagregador o un estudio en la nube). Si ya tienes una aplicación que utiliza una API de estilo “chat completions”, la migración consiste principalmente en cambiar el nombre del modelo y verificar los límites de contexto y herramientas.",{"id":284,"data":285,"type":288},"qwen35p-code1",{"code":286,"language":287},"\u002F\u002F Ejemplo mínimo (pseudo): reemplaza el endpoint\u002FSDK según el proveedor\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: \"Eres un agente que completa tareas.\" },\n    { role: \"user\", content: \"Revisa este repositorio y sugiere 5 mejoras de seguridad.\" }\n  ]\n});\n\nconsole.log(res.choices[0].message.content);","javascript","code",{"id":290,"data":291,"type":42},"qwen35p-usecases",{"text":292,"level":244},"Casos de uso donde Qwen 3.5-Plus tiene una ventaja real",{"id":294,"data":295,"type":270},"qwen35p-usecases-list",{"items":296,"style":302},[297,298,299,300,301],"\u003Cb>RAG agéntico en grandes corpus\u003C\u002Fb>: el contexto de 1M + el uso de herramientas reduce la necesidad de resúmenes agresivos.","\u003Cb>Codificación a nivel de repositorio\u003C\u002Fb>: análisis de múltiples archivos + generación de PRs con un plan consistente (menos “parches aleatorios”).","\u003Cb>Automatización de UI\u002FQA\u003C\u002Fb>: entrada multimodal + dirección de “agente visual” para pruebas de extremo a extremo y reproducción de errores a partir de grabaciones de pantalla\u002Fcapturas.","\u003Cb>Análisis de operaciones\u002Fincidentes\u003C\u002Fb>: grandes volúmenes de logs + ejecución de runbooks con herramientas (búsqueda, consulta, tickets).","\u003Cb>Agente de flujo de trabajo de datos\u003C\u002Fb>: generación de SQL, validación de resultados, correcciones iterativas — todo en una sola sesión sin perder el contexto.","ordered",{"id":304,"data":305,"type":42},"qwen35p-tradeoffs",{"text":306,"level":244},"Compromisos (para no caer en la trampa del hype)",{"id":308,"data":309,"type":270},"qwen35p-tradeoffs-list",{"items":310,"style":269},[311,312,313],"\u003Cb>Contexto de 1M ≠ 1M de “memoria perfecta”\u003C\u002Fb>: cuanto mayor sea la entrada, más cuidado debes tener con la estructura (seccionamiento, índice, plan de recuperación).","\u003Cb>El comportamiento agéntico requiere barandillas (guardrails)\u003C\u002Fb>: es obligatorio añadir una capa de políticas (herramientas permitidas, límite de velocidad, sandbox), registro y reproducción.","\u003Cb>Alojado vs pesos abiertos\u003C\u002Fb>: Plus como modelo alojado es excelente para la latencia\u002Festabilidad, pero las variantes de pesos abiertos son mejores para la privacidad y el control local (on-prem), aunque con una mayor carga operativa.",{"id":315,"data":316,"type":239},"qwen35p-bottomline",{"text":317},"\u003Cb>Conclusión:\u003C\u002Fb> Qwen 3.5-Plus es una señal de que la carrera se está desplazando de “quién es más inteligente en el chat” a “quién ejecuta flujos de trabajo complejos de manera más fiable”. Si estás creando agentes en producción, vale la pena probarlo, especialmente cuando el cuello de botella es el contexto, el uso de herramientas y la estabilidad a través de múltiples pasos.","2.31","Descubre las características y beneficios innovadores de Qwen 3.5-Plus de Alibaba, una IA de código abierto que cambia las reglas del juego para desarrolladores.","\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":327,"de":328,"sr":329,"es":330,"fr":331,"it":332,"ru":333,"zh":334},"\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",[336,340,344,348],{"id":337,"name":338,"slug":339},94,"Runbook: Lanzamiento","release-runbook",{"id":341,"name":342,"slug":343},106,"Runbook: Rollback de IA","ai-rollback",{"id":345,"name":346,"slug":347},88,"Versionado (prompts, modelos)","versioning",{"id":349,"name":350,"slug":351},87,"Playbook: LLMOps","llmops",{"id":353,"login":354,"email":355,"displayName":356},"20","rooth8233","","Aleksandar Stajić",[358,427],{"lang":359,"title":360,"content":361,"contentJson":362,"excerpt":426},"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":363,"blocks":364,"version":318},1771533358356,[365,368,371,374,377,381,384,392,394,397,400,403,406,414,417,423],{"id":231,"data":366,"type":42},{"text":367,"level":234},"Qwen 3.5-Plus: Open-source \"agentic\" AI that removes friction for developers in complex tasks",{"id":236,"data":369,"type":239},{"text":370},"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":241,"data":372,"type":42},{"text":373,"level":244},"Why this is important for developers",{"id":246,"data":375,"type":239},{"text":376},"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":250,"data":378,"type":255},{"text":379,"caption":380,"alignment":254},"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":257,"data":382,"type":42},{"text":383,"level":244},"Key innovations (Qwen 3.5-Plus in practice)",{"id":261,"data":385,"type":270},{"items":386,"style":269},[387,388,389,390,391],"\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":272,"data":393,"type":274},{},{"id":276,"data":395,"type":42},{"text":396,"level":244},"How to try it quickly (without further ado)",{"id":280,"data":398,"type":239},{"text":399},"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":284,"data":401,"type":288},{"code":402,"language":287},"\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":290,"data":404,"type":42},{"text":405,"level":244},"Use cases where Qwen 3.5-Plus offers a real advantage",{"id":294,"data":407,"type":270},{"items":408,"style":302},[409,410,411,412,413],"\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":304,"data":415,"type":42},{"text":416,"level":244},"Trade-offs (to avoid falling into the hype trap)",{"id":308,"data":418,"type":270},{"items":419,"style":269},[420,421,422],"\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":315,"data":424,"type":239},{"text":425},"\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":224,"content":226,"contentJson":428,"excerpt":319},{"time":228,"blocks":429,"version":318},[430,432,434,436,438,440,442,445,447,449,451,453,455,458,460,463],{"id":231,"data":431,"type":42},{"text":233,"level":234},{"id":236,"data":433,"type":239},{"text":238},{"id":241,"data":435,"type":42},{"text":243,"level":244},{"id":246,"data":437,"type":239},{"text":248},{"id":250,"data":439,"type":255},{"text":252,"caption":253,"alignment":254},{"id":257,"data":441,"type":42},{"text":259,"level":244},{"id":261,"data":443,"type":270},{"items":444,"style":269},[264,265,266,267,268],{"id":272,"data":446,"type":274},{},{"id":276,"data":448,"type":42},{"text":278,"level":244},{"id":280,"data":450,"type":239},{"text":282},{"id":284,"data":452,"type":288},{"code":286,"language":287},{"id":290,"data":454,"type":42},{"text":292,"level":244},{"id":294,"data":456,"type":270},{"items":457,"style":302},[297,298,299,300,301],{"id":304,"data":459,"type":42},{"text":306,"level":244},{"id":308,"data":461,"type":270},{"items":462,"style":269},[311,312,313],{"id":315,"data":464,"type":239},{"text":317},"Post erfolgreich abgerufen",{"items":467,"source":507,"manualIds":508,"manualMatchedIds":509},[468,474,481,486,493,500],{"id":469,"slug":470,"title":471,"excerpt":472,"featuredImage":320,"publishedAt":473},"445","qwen-3-6-in-production-release-runbook-ai-rollback-and-llmops-versioning","Qwen 3.6 en producción: Runbook de lanzamiento, rollback de IA y versionado de LLMOps","Qwen 3.6 no es solo otra actualización de modelo. Es un evento de lanzamiento, un escenario de reversión y un problema de versionado al mismo tiempo. Este artículo explica cómo debe manejarse Qwen 3.6 en producción a través de la disciplina de LLMOps, la trazabilidad de prompts y modelos, el despliegue controlado y la preparación para la reversión basada en evidencia.","2026-05-04T02:49:00.000Z",{"id":475,"slug":476,"title":477,"excerpt":478,"featuredImage":479,"publishedAt":480},"493","mlops-vs-llmops-what-changes-when-the-model-is-an-llm","MLOps vs LLMOps: qué cambia cuando el modelo es un LLM","MLOps opera sistemas de aprendizaje automático; LLMOps extiende esas prácticas a prompts, contexto, recuperación, proveedores, herramientas, evaluaciones y comportamiento en tiempo de ejecución en torno a modelos de lenguaje grandes.","\u002Fuploads\u002F2026\u002F10\u002Fmlops-vs-llmops-what-changes-when-the-model-is-an-llm-1791487319869-2v7hxo.webp","2026-10-08T15:20:00.000Z",{"id":482,"slug":483,"title":483,"excerpt":10,"featuredImage":484,"publishedAt":485},"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":487,"slug":488,"title":489,"excerpt":490,"featuredImage":491,"publishedAt":492},"459","ollama-is-not-the-product-building-production-ready-open-llm-applications","Ollama no es el producto: Construcción de aplicaciones de LLM abiertos listas para producción","Ejecutar un modelo local con Ollama es fácil. Construir una aplicación Open-LLM lista para producción es más difícil: requiere RAG, control de acceso, abstracción de proveedores, evaluación, registro, disciplina de despliegue y una capa de aplicación controlada alrededor del modelo.","\u002Fuploads\u002F2026\u002F06\u002Follama-is-not-the-product-building-production-ready-open-llm-applications-1782679361640-h0usqf.webp","2026-06-28T16:39:00.000Z",{"id":494,"slug":495,"title":496,"excerpt":497,"featuredImage":498,"publishedAt":499},"496","why-does-ai-retrieve-some-sources-and-ignore-others-the-retrieval-authority-layer","¿Por qué la IA recupera algunas fuentes e ignora otras? — La capa de autoridad de recuperación","Encontrar un documento relevante no es suficiente. Los sistemas de IA también necesitan distinguir las fuentes que son actuales, autorizadas, aplicables y rastreables de las fuentes que solo parecen similares. La Capa de Autoridad de Recuperación define ese límite de selección de fuentes.","\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":501,"slug":502,"title":503,"excerpt":504,"featuredImage":505,"publishedAt":506},"488","what-is-context-engineering-what-the-model-receives-before-it-answers","¿Qué es la ingeniería de contexto? Lo que el modelo recibe antes de responder","La ingeniería de contexto diseña qué información recibe un modelo de IA antes de la inferencia, incluidos los prompts, la recuperación, la memoria, el estado de la aplicación, los resultados de las herramientas y el historial de conversación.","\u002Fuploads\u002F2026\u002F10\u002Fwhat-is-context-engineering-what-the-model-receives-before-it-answers-1791480653258-018kcv.webp","2026-10-08T13:29:00.000Z","fallback",[],[]]