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
Organization: Qwen

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.