Prior Labs Releases TabPFN-3.5: Tabular Foundation Model Surpassing Kaggle Competition Winners Out of the Box

Prior Labs has released TabPFN-3.5, a tabular data foundation model that exceeds the performance of the winning solution on the Otto Kaggle competition using only default settings and no task-specific tuning. TabPFN-3.5 continues the prior TabPFN line's approach of treating tabular prediction as in-context learning, requiring no gradient-based fine-tuning on new datasets. For ML engineers working with structured/tabular data — still the dominant data format in enterprise applications — this represents a meaningful advance over both gradient-boosted tree ensembles and earlier foundation model approaches. The model is particularly useful for teams that need strong baselines quickly or lack the resources for extensive hyperparameter search. Developers should benchmark TabPFN-3.5 against XGBoost and LightGBM on their own tabular datasets as a first step.