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One Model, Infinite Predictions

Pre-trained tabular foundation models for making predictions on structured data.
Get best-in-class results in seconds - no tuning, no ML pipelines

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TabPFN–3

New

Our frontier model for most demanding prediction tasks. Up to 1M rows in 0.2s. Supercharged by Thinking mode.

Output

Python

>>> tabpfn.predict(X_test)
array(['No', 'Yes', 'No', 'Yes', 'No'])
>>> tabpfn.predict_proba(X_test)
array([[0.92, 0.08],
       [0.15, 0.85],
       [0.78, 0.22],
       [0.03, 0.97],
       [0.88, 0.12]])

TabPFN-3 by the numbers

Average accuracy across 51 OpenML datasets

Default

Tuned (4 hours)

Tuned + Ensembled (4 hours)

93% win rate over classic ML

On TabArena, leading benchmark for structured data tasks

0.2s for predictions on 1M samples

Fast inference for real-world predictive workflows

+420 ELO with Thinking mode

TabPFN-3.5-Thinking beats AutoML in 80% of the cases

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Trusted across industries

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40% Improvement in anomaly detection to advance predictive maintenance.

Isabel Fernando, Innovation Manager, Hitachi Rail

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TabPFN has the potential to fundamentally transform how banks approach prediction tasks.

Maksims Volkovs, Senior Vice President and Chief AI Scientist, Layer 6 AI at TD

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This collaboration enables us to derive actionable clinical insights from complex proteomic datasets.

Simon Meier, CEO, Oxford Cancer Analytics

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90% accuracy at cancer patients identification using peripheral blood

Michael Goldberg, PhD, VP R&D at BostonGene

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22 of 24 outcomes correctly predicted

Surgeon Commander Tim Scott, Consultant Anaesthetist, UHNM NHS Trust

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TabPFN presents an exciting opportunity to adopt tabular foundation models and unlock their potential to serve more customers with greater precision.

Dr. Dietrich Eherler, Head of Statistical Analysis and Development, Creditplus Bank AG

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TabPFN performs surprisingly well with minimal training data. This capability is particularly valuable for fraud detection, where labeled examples are limited.

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Old models were worse than TabPFN in MAPE. Now, our spend forecasting is more accurate and less manual.

Hendrik Dohme, Data Scientist, Exito

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Production-grade predictions - typically in seconds.

Dael Williamson, EMEA CTO, Databricks

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For Businesses

Finance

Healthcare

Industrials

Energy

Tech

Algorithmic traders forecasting directional price movements of assets.

Predicting automotive finance fraud cases and reducing bad losses.

Credit risk modelling for mortgage approvals and SME banking.

How TabPFN is Transforming Financial Risk Management

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Open source models used at

Cancer risk prediction in hospitalized patients based on biomarkers.

Classifying genetic variants leading to personalized medicine.

Alzheimers disease prediction for hospitalized patients.

TabPFN helps clinicians predict intubation - before it’s too late

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Open source models used at

Gas pipelines steel grade classifications for compliance.

Predictive maintenance for machinery using inspection data.

Quality control in manufacturing through automated defect detection.

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Open source models used at

Power and electricity traders using time-series forecasting for arbitrage trading.

Energy providers classifying incoming meter data for customer billing.

Monitoring grid performance to identify inefficiencies and anomalies.

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Open source models used at

Predicting customers likely to churn in the next 180 days.

Accurately predicting media spending for marketing campaigns.

Generating highly accurate synthetic survey data based on existing samples.

How Exito Transformed Media Spend Forecasting with TabPFN

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Open source models used at

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Predictions, where you need them

TabPFN fits into your stack - API, private cloud, or agents. Same frontier model, on your terms.

API

Get best results with API-exclusive Thinking mode.

VPC

Deploy TabPFN-3 inside your own cloud environment.

Agents

Supercharge your agents with structured data intelligence.

Start building with TabPFN today