InternVL2_5-26B-MPO is a multimodal large language model (MLLM) that builds upon InternVL2.5 and improves model performance through Mixed Preference Optimization (MPO). The model can handle multimodal data, including images and text, and is widely applied in scenarios such as image captioning and visual question answering. Its significance lies in its ability to understand and generate text closely related to image content, pushing the boundaries of multimodal AI. Background information on the product includes its exceptional performance in multimodal tasks and evaluation results on the OpenCompass Leaderboard. This model provides researchers and developers with a powerful tool to explore and realize the potential of multimodal AI.