TabPFN-2.5

TabPFN-2.5 by Prior Labs is the worlds leading Tabular Foundation Model. It ranks first on the popular TabArena benchmark for classification and regression tasks, outperforming tree-based models and ensembles tuned for more than 4 hours on datasets up to 50,000 samples and 2,000 features. TabPFN is a pretrained transformer trained on hundreds of millions of synthetic prediction tasks, allowing it to generalize across thousands of use cases in a single forward pass. This enables fast, accurate predictions with minimal preprocessing. The model handles mixed feature types (text, numerical, categorical), missing values, uninformative features, and outliers. 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 can be used for unsupervised workflows such as synthetic data generation, uncertainty estimation, and learning tabular embeddings. TabPFN-2.5 is the third generation of the TabPFN models previously published in Nature. This TabPFN-2.5 model package is free to use under the non-commercial conditions as specified in the model license.