| Title: |
Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters |
| Authors: |
Huang, Ailin; Li, Ang; Kong, Aobo; Wang, Bin; Jiao, Binxing; Dong, Bo; Wang, Bojun; Chen, Boyu; Li, Brian; Ma, Buyun; Su, Chang; Miao, Changxin; Wan, Changyi; Lou, Chao; Hu, Chen; Xu, Chen; Yu, Chenfeng; Feng, Chengting; Yao, Chengyuan; Han, Chunrui; Ma, Dan; Shi, Dapeng; Jiang, Daxin; Ma, Dehua; Sun, Deshan; Qi, Di; Liu, Enle; Zhang, Fajie; Wan, Fanqi; Huang, Guanzhe; Yan, Gulin; Cao, Guoliang; Li, Guopeng; Cheng, Han; Guo, Hangyu; Zhang, Hanshan; Nie, Hao; Jia, Haonan; Lv, Haoran; Zhou, Hebin; Lv, Hekun; Wang, Heng; Shum, Heung-Yeung; Huang, Hongbo; Peng, Hongbo; Zhou, Hongyu; Wang, Hongyuan; Chen, Houyong; Zhu, Huangxi; Wu, Huimin; Guo, Huiyong; Wang, Jia; Zhou, Jian; Sun, Jianjian; Wu, Jiaoren; Zhang, Jiaran; Lv, Jiashu; Liu, Jiashuo; Fu, Jiayi; Liu, Jiayu; Cheng, Jie; Luo, Jie; Yang, Jie; Zhou, Jie; Hou, Jieyi; Bai, Jing; Hu, Jingcheng; Xie, Jingjing; Wu, Jingwei; Zhang, Jingyang; Zhou, Jishi; Liu, Junfeng; Lin, Junzhe; Lo, Ka Man; Liang, Kai; Liu, Kaibo; Tan, Kaijun; Yan, Kaiwen; Li, Kaixiang; An, Kang; Lin, Kangheng; Yang, Lei; Lv, Liang; Zhao, Liang; Chen, Liangyu; Shi, Lieyu; Tan, Liguo; Lin, Lin; Chen, Lina; Ma, Luck; Ren, Mengqiang; Li, Michael; Li, Ming; Li, Mingliang; Zhang, Mingming; Chen, Mingrui; Huang, Mitt; Wang, Na; Liu, Peng; Han, Qi; Zhao, Qian; He, Qinglin; Du, Qinxin; Wu, Qiuping; Sun, Quan; Yang, Rongqiu; Miao, Ruihang; Han, Ruixin; Wan, Ruosi; Guo, Ruyan; Wang, Shan; Pang, Shaoliang; Yang, Shaowen; Fan, Shengjie; Shang, Shijie; Yang, Shiliang; Li, Shiwei; Tian, Shuangshuang; Liu, Siqi; Wu, Siye; Chen, Siyu; Yuan, Song; Cao, Tiancheng; Yue, Tianchi; Cheng, Tianhao; Li, Tianning; Luo, Tingdan; You, Wang; Ji, Wei; Yuan, Wei; Zhang, Wei; Wu, Weibo; Xie, Weihao; Sun, Wen; Deng, Wenjin; Zheng, Wenzhen; Xie, Wuxun; Wang, Xiangfeng; Kong, Xiangwen; Liu, Xiangyu; Zhang, Xiangyu; Yang, Xiaobo; Liu, Xiaojia; Yuan, Xiaolan; Jiao, Xiaoran; Ren, Xiaoxiao; Zhang, Xiaoyun; Li, Xin; Liu, Xin; Wu, Xin; Chen, Xing; Yang, Xingping; Wang, Xinran; Zhao, Xu; He, Xuan; Feng, Xuanti; Cai, Xuedan; Zhou, Xuqiang; Yu, Yanbo; Li, Yang; Xu, Yang; Lai, Yanlin; Xu, Yanming; Wang, Yaoyu; Shen, Yeqing; Zhu, Yibo; Lv, Yichen; Cao, Yicheng; Gong, Yifeng; Yang, Yijing; Yang, Yikun; Zhao, Yin; Zhao, Yingxiu; Zhang, Yinmin; Zhang, Yitong; Zhang, Yixuan; Chen, Yiyang; Zhao, Yongchi; Long, Yongshen; Wang, Yongyao; Guan, Yousong; Zhou, Yu; Peng, Yuang; Ding, Yuanhao; Fan, Yuantao; Lu, Yuanwei; Yang, Yuanzhen; Luo, Yuchu; Zhao, Yudi; Peng, Yue; Lin, Yueqiang; Lu, Yufan; Zhao, Yuling; Ju, Yunzhou; Zhang, Yurong; Li, Yusheng; Yang, Yuxiang; Chen, Yuyang; Cai, Yuzhu; Weng, Zejia; Hong, Zetao; Li, Zexi; Xie, Zhe; Ge, Zheng; Gong, Zheng; Zeng, Zheng; Lu, Zhenyi; Huang, Zhewei; Chang, Zhichao; Huang, Zhiguo; Hu, Zhiheng; Yang, Zidong; Wang, Zili; Ren, Ziqi; Zhang, Zixin; Wang, Zixuan |
| Publication Year: |
2026 |
| Collection: |
ArXiv.org (Cornell University Library) |
| Subject Terms: |
Computation and Language; Artificial Intelligence |
| Description: |
We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most when building agents: sharp reasoning and fast, reliable execution. Step 3.5 Flash pairs a 196B-parameter foundation with 11B active parameters for efficient inference. It is optimized with interleaved 3:1 sliding-window/full attention and Multi-Token Prediction (MTP-3) to reduce the latency and cost of multi-round agentic interactions. To reach frontier-level intelligence, we design a scalable reinforcement learning framework that combines verifiable signals with preference feedback, while remaining stable under large-scale off-policy training, enabling consistent self-improvement across mathematics, code, and tool use. Step 3.5 Flash demonstrates strong performance across agent, coding, and math tasks, achieving 85.4% on IMO-AnswerBench, 86.4% on LiveCodeBench-v6 (2024.08-2025.05), 88.2% on tau2-Bench, 69.0% on BrowseComp (with context management), and 51.0% on Terminal-Bench 2.0, comparable to frontier models such as GPT-5.2 xHigh and Gemini 3.0 Pro. By redefining the efficiency frontier, Step 3.5 Flash provides a high-density foundation for deploying sophisticated agents in real-world industrial environments. ; Technical report for Step 3.5 Flash |
| Document Type: |
text |
| Language: |
unknown |
| Relation: |
http://arxiv.org/abs/2602.10604 |
| Availability: |
http://arxiv.org/abs/2602.10604 |
| Accession Number: |
edsbas.D41F442 |
| Database: |
BASE |