OpenBMB is an open-source large model community started by Tsinghua University's NLP lab and ModelBest that releases the MiniCPM model family.
https://www.openbmb.cn →OpenBMB has released MiniCPM-OCR 1.0, a 1.3B-parameter model and the first OCR-specific model in the MiniCPM-V/o family. It scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2, and ships under Apache 2.0.
OpenBMB has released MiniCPM-V 4.7 1B, a 1B-parameter multimodal model for single-image, multi-image and video understanding on phones, tablets and local hardware. OpenBMB claims it substantially outperforms Qwen3.5 0.8B in overall score and inference efficiency in Instruct mode. Benchmark figures, context window and training cutoff are not given in the available model card text.
OpenBMB has released MiniCPM-V 4.7 35B-A3B, the first mixture-of-experts model in the MiniCPM-V series, with open weights on Hugging Face. The company claims it runs at 13+ tokens/s using 3 GB of memory on an iPhone 17 Pro Max, and that it beats Qwen3.6 35B-A3B on overall score in less inference time in Instruct mode.