Prior Labs Releases TabPFN-3.5: A Tabular Foundation Model That Beats the Winning Otto Kaggle Solution With Default Settings
Prior Labs has released TabPFN-3.5, the newest version of its tabular foundation model. It predicts on a table in a forward pass, with no per-dataset training or tuning. Prior Labs reports first place across 7 tabular benchmarks. A separate demonstration shows it beating the winning solution of a famous 2015 Kaggle competition. Deployable? Yes, with a license. Open weights run locally for research, evaluation and Kaggle, but production use needs Prior Labs’ API or a commercial license.
Prior Labs released TabPFN-3.5, a tabular foundation model that can make predictions on spreadsheet-like data without requiring training or tuning for each dataset. The model achieved a score that beats the winning solution from a 2015 Kaggle competition on product classification, using only raw data and default settings in about one minute. TabPFN-3.5 also placed first across seven tabular benchmarks and grows to 220 million parameters with improved encodings and preprocessing compared to its predecessor. Open-weight versions are available for research and evaluation, while production use requires a commercial license or access to Prior Labs' API.
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