Intel / article

Openbmb Launches Minicpm V 4 6 1 3b Instruct

Why it matters

A 1.3B Apache 2.0 vision- with video input and very low token use is relevant for on-device work.

Key takeaways · AI-distilled

MiniCPM-V 4.6 1.3B is dense, so total and active parameters are both 1.3B. It sits 3 points above Qwen3.5 0.8B (non-reasoning) and 2 below Qwen3.5 2B (non-reasoning), a new Pareto point for intelligence versus parameters.
It scores 38% on MMMU-Pro, the highest visual reasoning score Artificial Analysis has measured for an open-weights model under 2B, ahead of LFM2.5-VL-1.6B at 27% and Qwen3.5 0.8B at 26%.
Its 5.4M output tokens to run the index is about 19x fewer than Qwen3.5 0.8B non-reasoning (101M) and the lowest Artificial Analysis has measured for any open model under 4B scoring 10 or more.
Knowledge recall is weak, as with other sub-2B models: its AA-Omniscience score is -85, near Qwen3.5 0.8B at -89. It has a 262K , BF16 weights and no confirmed API providers at release.

Terms in this piece · Glossary

benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
multimodal — A model that works with more than text — reading images, audio, or video, and sometimes generating them too.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.

Read the source artificialanalysis.ai