In this work, we introduce the Qwen-VL series, a set of large-scalevision-language models (LVLMs) designed to perceive and understand both textsand images. Starting from the Qwen-LM as a foundation, we endow it with visualcapacity by the meticulously designed (i) visual receptor, (ii) input-outputinterface, (iii) 3-stage training pipeline, and (iv) multilingual multimodalcleaned corpus. Beyond the conventional image description andquestion-answering, we implement the grounding and text-reading ability ofQwen-VLs by aligning image-caption-box tuples. The resulting models, includingQwen-VL and Qwen-VL-Chat, set new records for generalist models under similarmodel scales on a broad range of visual-centric benchmarks (e.g., imagecaptioning, question answering, visual grounding) and different settings (e.g.,zero-shot, few-shot). Moreover, on real-world dialog benchmarks, ourinstruction-tuned Qwen-VL-Chat also demonstrates superiority compared toexisting vision-language chatbots. Code, demo and models are available athttps://github.com/QwenLM/Qwen-VL.