AndesVL Technical Report: An Efficient Mobile-side Multimodal Large
  Language Model
Authors: name: Zhiwei Jin; name: Xiaohui Song; name: Nan Wang; name: Yafei Liu; name: Chao Li; name: Xin Li; name: Ruichen Wang; name: Zhihao Li; name: Qi Qi; name: Long Cheng; name: Dongze Hao; name: Quanlong Zheng; name: Yanhao Zhang; name: Haobo Ji; name: Jian Ma; name: Zhitong Zheng; name: Zhenyi Lin; name: Haolin Deng; name: Xin Zou; name: Xiaojie Yin; name: Ruilin Wang; name: Liankai Cai; name: Haijing Liu; name: Yuqing Qiu; name: Ke Chen; name: Zixian Li; name: Chi Xie; name: Huafei Li; name: Chenxing Li; name: Chuangchuang Wang; name: Kai Tang; name: Zhiguang Zhu; name: Kai Tang; name: Wenmei Gao; name: Rui Wang; name: Jun Wu; name: Chao Liu; name: Qin Xie; name: Chen Chen; name: Haonan Lu
Published: 2025-10-13 15:04:38

Abstract: In recent years, while cloud-based MLLMs such as QwenVL, InternVL, GPT-4o,
Gemini, and Claude Sonnet have demonstrated outstanding performance with
enormous model sizes reaching hundreds of billions of parameters, they
significantly surpass the limitations in memory, power consumption, and
computing capacity of edge devices such as mobile phones. This paper introduces
AndesVL, a suite of mobile-side MLLMs with 0.6B to 4B parameters based on
Qwen3's LLM and various visual encoders. We comprehensively outline the model
architectures, training pipeline, and training data of AndesVL, which achieves
first-tier performance across a wide range of open-source benchmarks, including
fields such as text-rich image understanding, reasoning and math, multi-image
comprehension, general VQA, hallucination mitigation, multilingual
understanding, and GUI-related tasks when compared with state-of-the-art models
of a similar scale. Furthermore, we introduce a 1+N LoR