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

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

Open multimodal Qwen MoE for local agents that need vision, audio, and code

Compact Qwen3.5 multimodal model for fast chat, reasoning, and tool use

Small Qwen3.5 multimodal model for low-cost chat, reasoning, and tool use

Smallest Qwen3.5 multimodal model for on-device chat, reasoning, and tool use

Pre-trained Qwen3.5 base checkpoint for fine-tuning and research, without instruction tuning

Pre-trained Qwen3.5 base checkpoint for fine-tuning and research, without instruction tuning

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

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

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

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

Large open Qwen multimodal MoE for visual agents and long technical tasks

Open vision-language MoE model for image and video understanding, documents, and visual agents

Multimodal reasoning model accepting text, images, video, and audio with text output