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.
Deployability and Licensing
Yes, with a license. Open weights run locally for research, evaluation, and Kaggle, but production use requires Prior Labs' API or a commercial license.
The Otto Result
The Otto Group Product Classification Challenge ran on Kaggle in 2015. It drew 3,505 teams competing for $10,000. Entrants sorted products into 9 categories using 93 obfuscated count features. Submissions were scored with multi-class log loss, where lower is better.
The winning solution came from Gilberto Titericz and Stanislav Semenov. Both have held the world #1 Kaggle grandmaster ranking. Their entry was a multi-layer stack of 36 models built on hand-crafted features.
Nick Erickson, co-creator of AutoGluon and an AI researcher at Prior Labs, has chased that score for years. According to Erickson, AutoGluon placed rank 23 in its 2020 paper. AutoGluon 1.0 reached rank 14 in 2023, and AutoGluon 1.6 reached rank 9 in August 2026.
The final stretch was the hardest. Moving from rank 50 to rank 10 cut log loss from 0.41 to 0.40. Reaching the winning 0.382 from rank 10 took a further 0.018, nearly double.
TabPFN-3.5 scores 0.375 on the private leaderboard. Erickson says it ran on raw data with default settings. It took about a minute on an RTX PRO 6000 GPU. The model was pretrained only on synthetic data and never saw Otto or any Kaggle dataset. A reproducible Kaggle notebook is public.
Benchmark Results
The technical report lists first place on TabArena, BeyondArena, STRABLE, MulTaBench, RelArena-α, TALENT, and ScoringBench. The top entry is not always the base model. TabPFN-3.5-Thinking holds first on TabArena and other leaderboards, while the base TabPFN-3.5 remains highly competitive. This milestone underscores how foundation models for tabular data are closing the gap with hand-crafted, competition-grade pipelines, now with zero per-dataset tuning.
via MarkTechPost