STEP3-VL-10B Technical Report
Authors: name: Ailin Huang, name: Chengyuan Yao, name: Chunrui Han, name: Fanqi Wan, name: Hangyu Guo, name: Haoran Lv, name: Hongyu Zhou, name: Jia Wang, name: Jian Zhou, name: Jianjian Sun, name: Jingcheng Hu, name: Kangheng Lin, name: Liang Zhao, name: Mitt Huang, name: Song Yuan, name: Wenwen Qu, name: Xiangfeng Wang, name: Yanlin Lai, name: Yingxiu Zhao, name: Yinmin Zhang, name: Yukang Shi, name: Yuyang Chen, name: Zejia Weng, name: Ziyang Meng, name: Ang Li, name: Aobo Kong, name: Bo Dong, name: Changyi Wan, name: David Wang, name: Di Qi, name: Dingming Li, name: En Yu, name: Guopeng Li, name: Haiquan Yin, name: Han Zhou, name: Hanshan Zhang, name: Haolong Yan, name: Hebin Zhou, name: Hongbo Peng, name: Jiaran Zhang, name: Jiashu Lv, name: Jiayi Fu, name: Jie Cheng, name: Jie Zhou, name: Jisheng Yin, name: Jingjing Xie, name: Jingwei Wu, name: Jun Zhang, name: Junfeng Liu, name: Kaijun Tan, name: Kaiwen Yan, name: Liangyu Chen, name: Lina Chen, name: Mingliang Li, name: Qian Zhao, name: Quan Sun, name: Shaoliang Pang, name: Shengjie Fan, name: Shijie Shang, name: Siyuan Zhang, name: Tianhao You, name: Wei Ji, name: Wuxun Xie, name: Xiaobo Yang, name: Xiaojie Hou, name: Xiaoran Jiao, name: Xiaoxiao Ren, name: Xiangwen Kong, name: Xin Huang, name: Xin Wu, name: Xing Chen, name: Xinran Wang, name: Xuelin Zhang, name: Yana Wei, name: Yang Li, name: Yanming Xu, name: Yeqing Shen, name: Yuang Peng, name: Yue Peng, name: Yu Zhou, name: Yusheng Li, name: Yuxiang Yang, name: Yuyang Zhang, name: Zhe Xie, name: Zhewei Huang, name: Zhenyi Lu, name: Zhimin Fan, name: Zihui Cheng, name: Daxin Jiang, name: Qi Han, name: Xiangyu Zhang, name: Yibo Zhu, name: Zheng Ge
Organization: StepFun

We present STEP3-VL-10B, a lightweight open-source foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence. STEP3-VL-10B is realized through two strategic shifts: first, a unified, fully unfrozen pre-training strategy on 1.2T multimodal tokens that integrates a language-aligned Perception Encoder with a Qwen3-8B decoder to establish intrinsic vision-language synergy; and second, a scaled post-training pipeline featuring over 1k iterations of reinforcement learning. Crucially, we implement Parallel Coordinated Reasoning (PaCoRe) to scale test-time compute, allocating resources to scalable perceptual reasoning that explores and synthesizes diverse visual hypotheses. Consequently, despite its compact 10B footprint, STEP3-VL-10B rivals or surpasses models 10times-20times larger (e.g., GLM-4.6V-106B, Qwen3-VL-235B) and top-tier proprietary flagships like Gemini 2.5 Pro and Seed-1.5-VL. Delivering best-in-class performance, it records 92.2% on MMBench and 80.11% on MMMU, while excelling in complex reasoning with 94.43% on AIME2025 and 75.95% on MathVision. We release the full model suite to provide the community with a powerful, efficient, and reproducible baseline.