Artificial Analysis@ArtificialAnlys

2026-05-12 06:57·52天前

AI 摘要

清华大学与ModelBest合作的OpenBMB发布了MiniCPM-V 4.6 1.3B Instruct模型。这款仅13亿参数的多模态小模型在Artificial Analysis智能指数上获得13分，成为2B参数以下开源模型中得分最高的，刷新了该规模模型的帕累托前沿。它在MMMU-Pro视觉推理基准上也达到38%，领先同类小模型。模型支持文本、图像和视频输入，并具有极高的令牌效率，运行测试仅需540万输出令牌，远低于对比模型。其权重已在Hugging Face以Apache 2.0许可证开源。知识回忆能力与其他2B以下模型相当，处于较低水平。

OpenBMB， a Tsinghua University / ModelBest open weights collaboration， has released MiniCPM-V 4.6 1.3B Instruct， a tiny， non-reasoning model that scores 13 on the Artificial Analysis Intelligence Index

This model sits 3 points ahead of Qwen3.5 0.8B （Non-reasoning， 10） and 2 points behind Qwen3.5 2B （Non-reasoning， 15） on the Intelligence Index， establishing a new Pareto-optimal point on our Intelligence vs. Total Parameters chart. Tiny models are useful for efficient inference and on-device use cases.

MiniCPM-V 4.6 1.3B Instruct is a vision-language model that supports text， image， and video input with text output. @OpenBMB is a China-based lab jointly founded in 2022 by Tsinghua University's NLP Lab and ModelBest Inc.

The model's weights have been released under an Apache 2.0 license on Hugging Face.

Key results：

➤ At 1.3B parameters， MiniCPM-V 4.6 1.3B Instruct scores 13 on the Artificial Analysis Intelligence Index， the highest for any open weights model under 2B parameters. The next-most-intelligent open weights model at comparable scale is Qwen3.5 0.8B （Reasoning， 11） and used 43x as many tokens to run the Intelligence Index； Qwen3.5 2B which scores 16 （Reasoning） and 15 （Non-reasoning） requires 1.7x as many parameters （2.27B）. MiniCPM-V 4.6 1.3B Instruct also tops sub-2B open weights on MMMU-Pro， scoring 38%.

➤ MiniCPM-V 4.6 1.3B Instruct extends the open weights Pareto frontier for Intelligence vs. Total Parameters. Because the model is dense， total and active parameter counts are both 1.3B， so it pushes both frontiers. The next-most-intelligent sub-2B model （Qwen3.5 0.8B （Reasoning）， 11） lands 2 points behind， despite also using a reasoning mode.

➤ MiniCPM-V 4.6 1.3B Instruct is highly token efficient， and used just 5.4M output tokens to run the Intelligence Index， ~19x fewer than Qwen3.5 0.8B （Non-reasoning， 101M） and ~43x fewer than Qwen3.5 0.8B （Reasoning， 233M）. This is the lowest output token count measured for any open weights model under 4B total parameters scoring 10 or above on the Index （next-lowest is Ministral 3 3B at 15.5M）.

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2026-05-12 06:57·52天前

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AI 摘要