MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and
  Training Recipe
Authors: name: Tianyu Yu; name: Zefan Wang; name: Chongyi Wang; name: Fuwei Huang; name: Wenshuo Ma; name: Zhihui He; name: Tianchi Cai; name: Weize Chen; name: Yuxiang Huang; name: Yuanqian Zhao; name: Bokai Xu; name: Junbo Cui; name: Yingjing Xu; name: Liqing Ruan; name: Luoyuan Zhang; name: Hanyu Liu; name: Jingkun Tang; name: Hongyuan Liu; name: Qining Guo; name: Wenhao Hu; name: Bingxiang He; name: Jie Zhou; name: Jie Cai; name: Ji Qi; name: Zonghao Guo; name: Chi Chen; name: Guoyang Zeng; name: Yuxuan Li; name: Ganqu Cui; name: Ning Ding; name: Xu Han; name: Yuan Yao; name: Zhiyuan Liu; name: Maosong Sun
Published: 2025-09-16 19:41:48

Abstract: Multimodal Large Language Models (MLLMs) are undergoing rapid progress and
represent the frontier of AI development. However, their training and inference
efficiency have emerged as a core bottleneck in making MLLMs more accessible
and scalable. To address the challenges, we present MiniCPM-V 4.5, an 8B
parameter model designed for high efficiency and strong performance. We
introduce three core improvements in model architecture, data strategy and
training method: a unified 3D-Resampler model architecture for highly compact
encoding over images and videos, a unified learning paradigm for document
knowledge and text recognition without heavy data engineering, and a hybrid
reinforcement learning strategy for proficiency in both short and long
reasoning modes. Comprehensive experimental results in OpenCompass evaluation
show that MiniCPM-V 4.5 surpasses widely used proprietary models such as
GPT-4o-latest, and significantly larger open-source models such as Qwen2.5-VL
72B. Notably, the strong performance is achieved with remarkable efficiency.
For example, on the widely adopted VideoMME benchmark, MiniCPM-V 4.5 achieves
state-of-the-art performance among models under 30B size, using just 46.7\% GPU
memory cost and 8.7\% inference time of Qwen2.5-VL 7B.