MetaCLIP is an open-source machine learning model utilized for joint representation learning of images and text. It filters the CLIP data through a simple algorithm without dependence on previous model filters, thereby improving data quality and transparency. MetaCLIP's key contributions include filter-free data filtering, transparent training data distributions, scalable algorithms, and standardized CLIP training setups. The model emphasizes the importance of data quality and provides pre-trained models to support researchers and developers in conducting controlled experiments and fair comparisons.