tabpfn-v3

TabPFN-3 by Prior Labs is the world's leading Tabular Foundation Model. It ranks first on the popular TabArena benchmark for classification and regression tasks in a single forward pass, outperforming tree-based models and ensembles tuned for more than 4 hours, and beating 8-hour-tuned gradient-boosted-tree baselines on datasets up to 1,000,000 samples. TabPFN is a pretrained transformer trained exclusively on synthetic prediction tasks, allowing it to generalize across thousands of use cases without retraining. This enables fast, accurate predictions with minimal preprocessing, while being up to 20x faster than TabPFN-2.5. The model handles text features, mixed feature types (numerical, categorical), missing values, uninformative features, and outliers, and now natively supports many-class classification - with up to 160 classes. It is an ideal default model for teams seeking reliable performance without costly tuning or retraining cycles. In addition to classification, regression, and time-series tasks, TabPFN-3 extends to relational data and unsupervised workflows such as synthetic data generation, uncertainty estimation, and learning tabular embeddings. This TabPFN-3 model package is free to use under the non-commercial conditions as specified in the model license.