AndesVL Technical Report: An Efficient Mobile-side Multimodal Large
  Language Model
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
68edbb69de1fee572713a810
Zhiwei Jin
68edbb69de1fee572713a811
Xiaohui Song
68edbb69de1fee572713a812
Nan Wang
68edbb69de1fee572713a813
Yafei Liu
68edbb69de1fee572713a814
Chao Li
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Xin Li
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Ruichen Wang
68edbb69de1fee572713a817
Zhihao Li
68edbb69de1fee572713a818
Qi Qi
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Long Cheng
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Dongze Hao
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Quanlong Zheng
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Yanhao Zhang
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Haobo Ji
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Jian Ma
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Zhitong Zheng
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Zhenyi Lin
68edbb69de1fee572713a821
Haolin Deng
68edbb69de1fee572713a822
Xin Zou
68edbb69de1fee572713a823
Xiaojie Yin
68edbb69de1fee572713a824
Ruilin Wang
68edbb69de1fee572713a825
Liankai Cai
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Haijing Liu
68edbb69de1fee572713a827
Yuqing Qiu
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Ke Chen
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Zixian Li
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Chi Xie
68edbb69de1fee572713a82b
Huafei Li
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Chenxing Li
68edbb69de1fee572713a82d
Chuangchuang Wang
68edbb69de1fee572713a82e
Kai Tang
68edbb69de1fee572713a82f
Zhiguang Zhu
68edbb69de1fee572713a830
Kai Tang
68edbb69de1fee572713a831
Wenmei Gao
68edbb69de1fee572713a832
Rui Wang
68edbb69de1fee572713a833
Jun Wu
68edbb69de1fee572713a834
Chao Liu
68edbb69de1fee572713a835
Qin Xie
68edbb69de1fee572713a836
Chen Chen
68edbb69de1fee572713a837
Haonan Lu
AndesVL Technical Report: An Efficient Mobile-side Multimodal Large
  Language Model
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
AndesVL, a suite of mobile-side MLLMs with reduced parameters, achieves top-tier performance across various benchmarks compared to similar-scale models.
MLLMs QwenVL InternVL GPT-4o Gemini Claude Sonnet AndesVL Qwen3 LLM visual encoders text-rich image understanding reasoning math multi-image comprehension general VQA hallucination mitigation multilingual understanding GUI-related tasks 1+N LoR