Baichuan-M3: Modeling Clinical Inquiry for Reliable Medical Decision-Making
Authors: name: Baichuan-M3 Team, name: Chengfeng Dou, name: Fan Yang, name: Fei Li, name: Jiyuan Jia, name: Qiang Ju, name: Shuai Wang, name: Tianpeng Li, name: Xiangrong Zeng, name: Yijie Zhou, name: Hongda Zhang, name: Jinyang Tai, name: Linzhuang Sun, name: Peidong Guo, name: Yichuan Mo, name: Xiaochuan Wang, name: Hengfu Cui, name: Zhishou Zhang
Organization: Baichuan Intelligent Technology

We introduce Baichuan-M3, a medical-enhanced large language model engineered to shift the paradigm from passive question-answering to active, clinical-grade decision support. Addressing the limitations of existing systems in open-ended consultations, Baichuan-M3 utilizes a specialized training pipeline to model the systematic workflow of a physician. Key capabilities include: (i) proactive information acquisition to resolve ambiguity; (ii) long-horizon reasoning that unifies scattered evidence into coherent diagnoses; and (iii) adaptive hallucination suppression to ensure factual reliability. Empirical evaluations demonstrate that Baichuan-M3 achieves state-of-the-art results on HealthBench, the newly introduced HealthBench-Hallu and ScanBench, significantly outperforming GPT-5.2 in clinical inquiry, advisory and safety. The models are publicly available at https://huggingface.co/collections/baichuan-inc/baichuan-m3.