Models

Open-weight experimental preview of the Qwen4 architecture: hybrid-attention MoE (125B total, 6B active) with vision encoder for coding, agent tasks, and image and video understanding

Dense 27B vision-language model for coding, agent tasks, and image and video understanding

Open-weight sparse MoE (2.4T total, 95B active), the open-weight twin of Qwen3.8 Max for coding, research, complex reasoning, and agentic workflows

Qwen vision-language model for visual reasoning, documents, and agent tasks

Open-weight Qwen coding model for agents, repository edits, and multi-turn tool use

Qwen vision-language instruct model for visual reasoning, documents, and agent tasks

Qwen instruction model for multilingual chat, reasoning, and tool use

Efficient Qwen thinking model for local reasoning, math, and coding agents

Multilingual text embedding model for retrieval, code search, classification, and clustering