InternVL2.5-MPO is a series of multimodal large language models based on InternVL2.5 and Mixed Preference Optimization (MPO). It excels in multimodal tasks by integrating the recently incrementally pre-trained InternViT with various pre-trained large language models (LLMs) such as InternLM 2.5 and Qwen 2.5, utilizing a randomly initialized MLP projector. This model series has been trained on the multimodal reasoning preference dataset MMPR, which contains approximately 3 million samples, enhancing the model's reasoning capabilities and answer quality through an effective data construction process and mixed preference optimization techniques.