TabPFN-3.5, a Tabular Foundation Model for messy real-world tables

In plain words

TabPFN-3.5 is a tabular foundation model designed to handle messy real-world data tables where rows are interconnected and features are imperfect. It ranks first on TabArena and BeyondArena benchmarks and is built for scenarios like insurance claims with reviewer notes, transaction data linked to merchants and customers, and scientific sensor readings. The tool addresses limitations of traditional machine learning approaches that assume clean, independent data. TabPFN-3.5-Fast offers a faster variant for latency-sensitive applications.

written from the facts on this page · September 2026

From the sources

Today, we are releasing TabPFN-3.5, which ranks first place on both TabArena and BeyondArena . We also update Thinking mode and Plus to make use of TabPFN-3.5. For latency-sensitive applications, we’re introducing TabPFN-3.5-Fast, available in alpha. Most machine learning assumes rows are independent, features are clean and there is enough data to train a gradient boosting tree. In reality, business data rarely fits this assumption. An insurance data table combines the claim amount with reviewer conclusions. Transactions are connected to merchants and customers. Scientific and industrial sensors collect hundreds of measurements, and data differs by location or time period. These cases so…from priorlabs.ai

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