Qwen3-VL Technical Report
Authors: name: Shuai Bai; name: Yuxuan Cai; name: Ruizhe Chen; name: Keqin Chen; name: Xionghui Chen; name: Zesen Cheng; name: Lianghao Deng; name: Wei Ding; name: Chang Gao; name: Chunjiang Ge; name: Wenbin Ge; name: Zhifang Guo; name: Qidong Huang; name: Jie Huang; name: Fei Huang; name: Binyuan Hui; name: Shutong Jiang; name: Zhaohai Li; name: Mingsheng Li; name: Mei Li; name: Kaixin Li; name: Zicheng Lin; name: Junyang Lin; name: Xuejing Liu; name: Jiawei Liu; name: Chenglong Liu; name: Yang Liu; name: Dayiheng Liu; name: Shixuan Liu; name: Dunjie Lu; name: Ruilin Luo; name: Chenxu Lv; name: Rui Men; name: Lingchen Meng; name: Xuancheng Ren; name: Xingzhang Ren; name: Sibo Song; name: Yuchong Sun; name: Jun Tang; name: Jianhong Tu; name: Jianqiang Wan; name: Peng Wang; name: Pengfei Wang; name: Qiuyue Wang; name: Yuxuan Wang; name: Tianbao Xie; name: Yiheng Xu; name: Haiyang Xu; name: Jin Xu; name: Zhibo Yang; name: Mingkun Yang; name: Jianxin Yang; name: An Yang; name: Bowen Yu; name: Fei Zhang; name: Hang Zhang; name: Xi Zhang; name: Bo Zheng; name: Humen Zhong; name: Jingren Zhou; name: Fan Zhou; name: Jing Zhou; name: Yuanzhi Zhu; name: Ke Zhu
Published: 2025-11-26 17:59:08

Abstract: We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks. It natively supports interleaved contexts of up to 256K tokens, seamlessly integrating text, images, and video. The model family includes both dense (2B/4B/8B/32B) and mixture-of-experts (30B-A3B/235B-A22B) variants to accommodate diverse latency-quality trade-offs. Qwen3-VL delivers three core pillars: (i) markedly stronger pure-text understanding, surpassing comparable text-only backbones in several cases; (ii) robust long-context comprehension with a native 256K-token window for both text and interleaved multimodal inputs, enabling faithful retention, retrieval, and cross-referencing across long documents and videos; and (iii) advanced multimodal reasoning across single-image, multi-image, and video tasks, demonstrating leading performance on comprehensive evaluations such as MMMU and visual-math benchmarks (e.g., MathVista and MathVision). Architecturally, we introduce three key upgrades: (i) an enhanced interleaved-MRoPE for stronger spatial-temporal modeling across images and video; (ii) DeepStack integration, which effectively leverages multi-level ViT features to tighten vision-language alignment; and (iii) text-based time alignment for video, evolving from T-RoPE to explicit textual timestamp alignment for more precise temporal grounding. Under comparable token budgets and latency constraints, Qwen3-VL achieves superior performance in both dense and Mixture-of-Experts (MoE) architectures. We envision Qwen3-VL serving as a foundational engine for image-grounded reasoning, agentic decision-making, and multimodal code intelligence in real-world workflows.