Authors: Xiaomi LLM-Core

Affiliation: Xiaomi

Description: We open-source MiMo-VL-7B-SFT and MiMo-VL-7B-RL, two powerful vision-language models
delivering state-of-the-art performance in both general visual understanding and multimodal
reasoning. MiMo-VL-7B-RL outperforms Qwen2.5-VL-7B on 35 out of 40 evaluated tasks, and
scores 59.4 on OlympiadBench, surpassing models with up to 78B parameters. For GUI grounding
applications, it sets a new standard with 56.1 on OSWorld-G, even outperforming specialized
models such as UI-TARS. Our training combines four-stage pre-training (2.4 trillion tokens) with
Mixed On-policy Reinforcement Learning (MORL) integrating diverse reward signals. We identify
the importance of incorporating high-quality reasoning data with long Chain-of-Thought into
pre-training stages, and the benefits of mixed RL despite challenges in simultaneous multi-domain
optimization. We also contribute a comprehensive evaluation suite covering 50+ tasks to promote
reproducibility and advance the field. The model checkpoints and full evaluation suite are
available at https://github.com/XiaomiMiMo/MiMo-VL.

Authors: InternVL team

Affiliation: Shanghai AI Laboratory & Sensetime & Tsinghua University

Email: czcz94cz@gmail.com

Description: InternVL Family: Closing the Gap to Commercial Multimodal Models with Open-Source Suites —— A Pioneering Open-Source Alternative to GPT-4V

Demo: https://internvl.opengvlab.com/
Code: https://github.com/OpenGVLab/InternVL
Model: https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5

Authors: Molmo Team

Affiliation: Allen Institute for Artificial Intelligence

Description: The 72B member of the Molmo family of open vision-language models developed by the Allen Institute for AI. Molmo models are trained on PixMo, a dataset of 1 million, highly-curated image-text pairs and have open source weights, training data, and training recipe.