Prior Labs Releases TabPFN-3.5: A Tabular Foundation Model That Beats the Winning Otto Kaggle Solution With Default Settings
Prior Labs releases TabPFN-3.5, a 220M-parameter open-weights tabular foundation model that beats the 2015 Otto Kaggle winning score with default settings.
Prior Labs released TabPFN-3.5, a tabular foundation model that predicts in a single forward pass without per-dataset training or tuning. The base model grew from 53M to 220M parameters with a single multitask checkpoint, learned Fourier features, and in-context ECDF rank encodings. It scores 0.375 on the 2015 Otto Kaggle private leaderboard versus the winning 0.382 and claims first place on seven tabular benchmarks including TabArena and BeyondArena. Open weights cover the base, Fast (84M), and Thinking variants, but production use requires the Prior Labs API or a commercial license.
- Base model scales from 53M to 220M parameters with one multitask checkpoint for classification and regression.
- Scores 0.375 log loss on the Otto Kaggle challenge, beating the 2015 winning 0.382 with default settings.
- Reports first place on seven tabular benchmarks including TabArena, BeyondArena, STRABLE, and TALENT.
- Family includes Fast (84M), API-only Plus, and Thinking variants with up to 12x speedups.
- Open weights are research-only; production requires Prior Labs API or a commercial license.
From the original article
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Prior Labs released TabPFN-3. 5, a tabular foundation model pretrained only on synthetic data that beats Otto's winning solution. The post Prior Labs Releases TabPFN-3.
By Asif Razzaq · marktechpost.com
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