4M is a research framework for training foundation models that handle arbitrary input-output modality combinations using masked token modeling and unified tokenization. The released 4M-7 and 4M-21 models perform diverse vision tasks including generation, detection, segmentation, and transfer to unseen tasks and modalities. Code, pretrained weights, and training infrastructure are open-sourced for reproducibility.