HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better
Authors: name: Gengluo Li, name: Xingyu Wan, name: Shangpin Peng, name: Weinong Wang, name: Hao Feng, name: Yongkun Du, name: Binghong Wu, name: Zheng Ruan, name: Zhiqiong Lu, name: Liang Wu, name: Pengyuan Lyu, name: Huawen Shen, name: Zibin Lin, name: Shijing Hu, name: Jieneng Yang, name: Hongbing Wen, name: Guanghua Yu, name: Hong Liu, name: Bochao Wang, name: Can Ma, name: Han Hu, name: Chengquan Zhang, name: Yu Zhou
Organization: Tencent

We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, information extraction, text-image translation, and multi-image document understanding within a single end-to-end VLM. Building upon the lightweight architecture of HunyuanOCR-1.0, HunyuanOCR-1.5 does not redesign the backbone, but systematically improves both efficiency and capability. For efficiency, we adapt DFlash to OCR decoding, significantly reducing the latency of long structured outputs such as dense documents, tables, and formulas while preserving output distribution. Powered by DFlash, HunyuanOCR-1.5 achieves a 6.37x Transformer inference speedup and a 2.14x speedup under vLLM, delivering the fastest inference among lightweight OCR VLMs. For capability, we propose Agentic Data Flow, an agent-driven data construction system that transforms model weaknesses into executable data requirements and autonomously performs material search, quality verification, and pipeline development. It substantially improves long-tail capabilities in ancient-script OCR, fine-grained chart and table parsing, multi-image text-centric QA, low-resource multilingual parsing, and document hallucination evaluation. HunyuanOCR-1.5 ranks among the top-tier end-to-end OCR solutions on OmniDocBench v1.6 while achieving new performance milestones across these long-tail tasks. Combined with an upgraded pretraining and post-training recipe, HunyuanOCR-1.5 further extends its capability in high-resolution, long-context, and multi-task scenarios. Experiments demonstrate faster inference, broader OCR capability coverage, and the deployment advantages of a lightweight end-to-end model. We will release the model weights and training code to support future research and real-world OCR applications.