OpenBMB launches MiniCPM-V 4.6 1.3B Instruct

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 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), which 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).

➤ MiniCPM-V 4.6 1.3B Instruct supports native multimodal input, including text, image, and video, and scores 38% on MMMU-Pro. This is the highest visual reasoning score measured for any open weights model under 2B parameters, ahead of LFM2.5-VL-1.6B (27%) and Qwen3.5 0.8B (Non-reasoning, 26%). Video input at this parameter scale is uncommon.

➤ Knowledge recall is low, in line with other sub-2B models. AA-Omniscience is -85, in the typical range for sub-2B non-reasoning models (Qwen3.5 0.8B (Non-reasoning) at -89, Exaone 4.0 1.2B (Non-reasoning) at -83), and 2 points behind Qwen3.5 2B (Non-reasoning) at -83 (1.7x the parameter count).

Additional model details:

➤ Size: 1.3B total parameters (dense)

➤ Context window: 262K

➤ Precision: BF16

➤ License: Apache 2.0

➤ Providers: No confirmed providers on release

MiniCPM-V 4.6 1.3B Instruct extends the open weights Pareto frontier on both Intelligence vs. Total Parameters and Intelligence vs. Active Parameters at the sub-2B scale.

MiniCPM-V 4.6 1.3B Instruct is highly token efficient, and used just 5.4M output tokens to run the Artificial Analysis Intelligence Index, the lowest output token count measured for any open weights model under 4B scoring 10 or above on the Intelligence Index.

MiniCPM-V 4.6 1.3B Instruct shows weaknesses on knowledge recall and hallucination avoidance. It scores -85 on AA-Omniscience, in the same range as other sub-2B non-reasoning models (Qwen3.5 0.8B (Non-reasoning) at -89, Exaone 4.0 1.2B (Non-reasoning) at -83).

Full results for the Intelligence Index and MMMU-Pro: