| Model | Max Rows | Max Features | Data Type | License | Primary Publication |
|---|---|---|---|---|---|
| TabPFN-2.5 | 100,000 | 2,000 | Mixed | TABPFN-2.5 License v1.0 | Model report |
| TabPFN-2 | 10,000 | 500 | Mixed | Prior Labs License / allows any commercial use | Nature (2025) |
| TabPFN (v1) | 1,000 | 100 | Numeric Only | Apache 2.0 | ICLR 2023 |
TabPFN-2.5
TabPFN-2.5 is the next generation of the tabular foundation model, designed to scale to significantly larger datasets. It expands the in-context learning capabilities of TabPFN to additional tabular problems. Key Features:- Scales to 100,000 samples and 2,000 features.
- Continues to support mixed data types and requires no preprocessing.
TabPFN-2.5 Technical Report
TabPFN-2
TabPFN-2 is the model published in Nature and represents a significant upgrade over v1. It was re-engineered to handle real-world, “messy” data. It remains a powerful and accessible foundation model for small to medium-sized datasets. Key Features:- Handles mixed data types (numerical and categorical) and missing values automatically.
- Scales to 10,000 samples and 500 features.
- Delivers SOTA performance, often outperforming tuned Gradient-Boosted Trees (GBTs) and AutoML systems on datasets within its size limit.
Nature Publication
TabPFN (v1)
This is the original TabPFN model presented at ICLR 2023. It introduced the concept of a Prior-data Fitted Network and demonstrated that a transformer pre-trained on synthetic data could achieve strong results on small tabular datasets within a second. Key Limitations:- Designed only for numerical features.
- Strictly limited to datasets with 1,000 samples and 100 features.
arXiv Paper
TabPFN-2.5 Model License
What does the non-commercial license mean for enterprise use?
What does the non-commercial license mean for enterprise use?
Can enterprises use TabPFN-2.5 for internal testing?
Can enterprises use TabPFN-2.5 for internal testing?
What is not allowed in enterprise testing?
What is not allowed in enterprise testing?
Can we use results from testing to inform product features or deployment strategy?
Can we use results from testing to inform product features or deployment strategy?
Can we evaluate TabPFN-2.5 for integration into our product?
Can we evaluate TabPFN-2.5 for integration into our product?
If we want to move from testing to production, what is required?
If we want to move from testing to production, what is required?
Do non-commercial rules also apply to derivative models or fine tuned versions?
Do non-commercial rules also apply to derivative models or fine tuned versions?
What about outputs generated by the model?
What about outputs generated by the model?
If we train a model from scratch without the weights, is that covered by this license?
If we train a model from scratch without the weights, is that covered by this license?
Can we use TabPFN-2.5 to train a commercial model indirectly?
Can we use TabPFN-2.5 to train a commercial model indirectly?
Where should we go if we want to deploy TabPFN in production or test it?
Where should we go if we want to deploy TabPFN in production or test it?
What about the previous TabPFNv2 release?
What about the previous TabPFNv2 release?
Who do we contact for commercial licensing?
Who do we contact for commercial licensing?