titleHunyuan-A13B Technical Report
authorsTencent Hunyuan Team, Ao Liu, Botong Zhou, Can Xu, Chayse Zhou, ChenChen Zhang, Chengcheng Xu, Chenhao Wang, Decheng Wu, Dengpeng Wu, Dian Jiao, Dong Du, Dong Wang, Feng Zhang, Fengzong Lian, Guanghui Xu, Guanwei Zhang, Hai Wang, Haipeng Luo, Han Hu, Huilin Xu, Jiajia Wu, Jianchen Zhu, Jianfeng Yan, Jiaqi Zhu, Jihong Zhang, Jinbao Xue, Jun Xia, Junqiang Zheng, Kai Liu, Kai Zhang, Kai Zheng, Kejiao Li, Keyao Wang, Lan Jiang, Lixin Liu, Lulu Wu, Mengyuan Huang, Peijie Yu, Peiqi Wang, Qian Wang, Qianbiao Xiang, Qibin Liu, Qingfeng Sun, Richard Guo, Ruobing Xie, Saiyong Yang, Shaohua Chen, Shihui Hu, Shuai Li, Shuaipeng Li, Shuang Chen, Suncong Zheng, Tao Yang, Tian Zhang, Tinghao Yu, Weidong Han, Weijie Liu, Weijin Zhou, Weikang Wang, Wesleye Chen, Xiao Feng, Xiaoqin Ren, Xingwu Sun, Xiong Kuang, Xuemeng Huang, Xun Cao, Yanfeng Chen, Yang Du, Zhen Yang, Yangyu Tao, Yaping Deng, Yi Shen, Yigeng Hong, Yiqi Chen
date2026-09-23
venuearXiv (preprint, not peer reviewed)
idarXiv:2609.27284
affiliationTencent Hunyuan
upvotes21 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf
abstractWe present Hunyuan-A13B, an open-source large language model based on a Mixture-of-Experts architecture. It contains 80 billion total parameters but activates only 13 billion during inference, balancing model capability, computational efficiency, and deployment cost. The model is pretrained on a rigorously filtered 20T-token corpus with enhanced STEM data curation, improving factual reliability and reasoning ability. High-quality supervised fine-tuning and large-scale reinforcement learning further enhance its overall performance. Hunyuan-A13B also introduces a dual-mode Chain-of-Thought framework that adapts reasoning depth to task complexity: fast thinking for routine queries and slow thinking for complex, multi-step problems. Evaluations show competitive performance across mathematics, science, programming, general language understanding, and agent tasks, often approaching that of much larger models. Its high inference throughput makes it suitable for latency-sensitive applications. We release Hunyuan-A13B to support open research and practical LLM deployment.

cite

@misc{team2026hunyuan,
  title={Hunyuan-A13B Technical Report},
  author={Tencent Hunyuan Team and Ao Liu and Botong Zhou and Can Xu and Chayse Zhou and ChenChen Zhang and Chengcheng Xu and Chenhao Wang and Decheng Wu and Dengpeng Wu and Dian Jiao and Dong Du and Dong Wang and Feng Zhang and Fengzong Lian and Guanghui Xu and Guanwei Zhang and Hai Wang and Haipeng Luo and Han Hu and Huilin Xu and Jiajia Wu and Jianchen Zhu and Jianfeng Yan and Jiaqi Zhu and Jihong Zhang and Jinbao Xue and Jun Xia and Junqiang Zheng and Kai Liu and Kai Zhang and Kai Zheng and Kejiao Li and Keyao Wang and Lan Jiang and Lixin Liu and Lulu Wu and Mengyuan Huang and Peijie Yu and Peiqi Wang and Qian Wang and Qianbiao Xiang and Qibin Liu and Qingfeng Sun and Richard Guo and Ruobing Xie and Saiyong Yang and Shaohua Chen and Shihui Hu and Shuai Li and Shuaipeng Li and Shuang Chen and Suncong Zheng and Tao Yang and Tian Zhang and Tinghao Yu and Weidong Han and Weijie Liu and Weijin Zhou and Weikang Wang and Wesleye Chen and Xiao Feng and Xiaoqin Ren and Xingwu Sun and Xiong Kuang and Xuemeng Huang and Xun Cao and Yanfeng Chen and Yang Du and Zhen Yang and Yangyu Tao and Yaping Deng and Yi Shen and Yigeng Hong and Yiqi Chen},
  year={2026},
  eprint={2609.27284},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2609.27284}
}