The Qwen2-VL models are large vision-language models (LVLMs) that can process visual and textual information, and they can be used for a variety of tasks including image and video understanding, document parsing, and agent tasks. The authors discuss the architecture of the Qwen2-VL models, including the Naive Dynamic Resolution mechanism and the Multimodal Rotary Position Embedding (M-RoPE), and they present experimental results demonstrating that the Qwen2-VL models achieve highly competitive performance on various benchmarks. Notably, the Qwen2-VL-72B model achieves results comparable to leading models such as GPT-4o and Claude3.5-Sonnet across various multimodal benchmarks. The paper also explores the scaling laws for LVLMs and demonstrates the impact of increasing model and data size on performance.