Qwen: Qwen3.5 Plus 2026-02-15

Qwen3.5 Plus 2026-02-15 is part of the Qwen3.5 family of vision-language models, designed for multimodal understanding that combines text, image, and video inputs into reliable textual responses. According to OpenRouter, the model is built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts components, an approach intended to raise inference efficiency while handling long sequences. It fits naturally into assistant-style applications such as drafting and editing text, answering questions, coding help, and multi-turn dialogue, and is also well suited to workflows that call external tools or services.

In practical terms, the model pairs its vision-language backbone with deep step-by-step reasoning, function calling, and structured output, making it a flexible choice for teams that need both reasoning and tool use in the same workflow. Independent benchmark snapshots reported by Buzzi.ai place it solidly across knowledge, code, and reasoning suites, including strong results on AIME 2025 and IFEval, which suggests reliable instruction following and mathematical problem solving alongside general knowledge tasks. With a very large context window that can hold multiple books or whole code repositories, it is particularly useful for long-document analysis, repository-scale code work, and sustained conversational sessions where the model needs to keep extensive history in view.