TabPFN-3.5 builds on TabPFN-3 and improves most on non-IID settings, where rows are not independent, features are not clean and data types are mixed. It excels at predictions on tables that are text-rich, have high-cardinality and high-dimensionality features, as well as grouped and temporal data. The family includes four versions of the models:
Model version
How it differs
Max training rows
Max columns
Availability
TabPFN-3.5
The base model for local inference. Supports numerical, categorical, and text features directly.
The most accurate offering, spends more compute for better predictions, and adds stronger handling of grouped and temporal data.
200,000
2000
Hosted API & tabpfn-client, VPC.
*The hosted TabPFN-3.5-Plus API also has text preprocessing limits: at most 50 million tokens in total and 2,500 characters per text value.For TabPFN-3.5, we recommend datasets with up to 6,000 columns; the model ceiling is 20,000 columns. Row and column limits trade off, so their maximum values cannot necessarily be used together. Use the many-class extension for classification tasks with more than 160 classes.
The model is licensed under TABPFN-3-5 License v1.0. Code under Apache 2.0.
TabPFN-3 supports numerical and categorical data, missing values, and up to 160 classes natively. The family includes a local base model, Plus for native text features, and Thinking for when compute matters less than accuracy.
Model version
How it differs
Max training rows
Max columns
Availability
TabPFN-3
The base model for local inference. Handles numerical and categorical features, as well as missing values.
1,000,000
2,000
Local tabpfn package, hosted API & tabpfn-client.
TabPFN-3-Plus
Adds native text handling for columns such as product descriptions, customer notes, and reviews.
The most accurate offering, spends more compute for better predictions.
200,000
2,000
Hosted API & tabpfn-client, VPC.
Row and column limits trade off, so their maximum values cannot necessarily be used together. Use the many-class extension for classification tasks with more than 160 classes.
The model is licensed under TABPFN-3 License v1.0. Code under Prior Labs License.
TabPFN-2.6 was the default model in TabPFN OSS package versions v7.0.0–v7.1.x. It shares the same architecture and dataset constraints as TabPFN-2.5 with improved benchmark performance.
Model version
How it differs
Max training rows
Max columns
Availability
TabPFN-2.6
Handles numerical and categorical features and missing values. Supports classification with up to 10 classes.
TabPFN-2.5 expanded support to larger datasets than TabPFNv2. The family includes a base model for numerical and categorical data and a hosted Plus model that also handles text.
Model version
How it differs
Max training rows
Max columns
Availability
TabPFN-2.5
Handles numerical and categorical features and missing values. Supports classification with up to 10 classes.
100,000
2,000
Local tabpfn package.
TabPFN-2.5-Plus
Adds text handling for columns such as product names, reviews, and descriptions without manual preprocessing.
TabPFNv2 is the model published in Nature and the first TabPFN to handle real-world messy data — mixed types, missing values, and categorical features automatically.
Model version
How it differs
Max training rows
Max columns
Availability
TabPFNv2
Best suited for smaller datasets. Handles numerical and categorical features and missing values, with up to 10 classes.
Does accepting the license share my datasets with Prior Labs?
No. Accepting the license unlocks the model weights download. Your datasets stay local and are never sent to Prior Labs, and the tabpfn package works offline once the weights are downloaded. See Offline use.
What does the non-commercial license mean for enterprise use?
Enterprises can download and experiment with the model internally without cost. This includes benchmarking, testing on internal datasets, exploring capabilities, and understanding fit for use cases. Any competitive benchmarking (i.e. for procurement), testing in deployment in production environments, use in workflows that support business decisions, client projects, or revenue generating activities requires a separate commercial license or API agreement.
Can enterprises use TabPFN-3.5 or TabPFN-3 for internal use cases?
Enterprises may run preliminary internal assessments on their own data for research and exploration. This includes testing on internal datasets, exploring capabilities and understanding fit for use-cases.
What is not allowed in enterprise testing?
You cannot use the model or its outputs for any activity that influences business decisions, evaluation of vendors, procurement decisions, or commercial workflows. This includes proof of value that affects internal budgeting or vendor selection. In those cases, a commercial license is required. For that, check out our pricing or contact our sales team.
Can we use results from testing to inform product features or deployment strategy?
Not without a commercial license. Any evaluation that influences commercial decisions or internal production planning is considered commercial use. For that, check out our pricing or contact our sales team.
Can we evaluate TabPFN-3.5 or TabPFN-3 for integration into our product?
You can run small scale internal experiments. If you intend to integrate TabPFN into a product or platform, the API or a commercial license is required. For that, check out our pricing or contact our sales team.
If we want to move from testing to production, what is required?
You will need a commercial license or API agreement. Check out our pricing or contact our sales team.
Do non-commercial rules also apply to derivative models or fine tuned versions?
Yes. The same restrictions apply to all derivatives. Fine tuned, modified, or otherwise adapted models remain non-commercial unless you secure a commercial license.
What about outputs generated by the model?
Outputs are yours, but they can only be used for non-commercial evaluation and research. Using them in production or for any business decisions requires a commercial license.
If we train a model from scratch without the weights, is that covered by this license?
If you do not use any TabPFN model weights, checkpoints, or other licensed model elements, then the non-commercial license does not apply to that separately developed model. However, if you use TabPFN (or its outputs) to train, guide, fine tune, or distill another model for commercial use, that is not permitted under this license.
Can we use TabPFN to train a commercial model indirectly?
No. Using TabPFN (or its outputs) to train, fine tune, distill, or otherwise improve a commercial model is not allowed without a commercial agreement.
Where should we go if we want to deploy TabPFN in production?
Use our API or obtain a commercial license. The API is the simplest path for production trials and product integration. For that, check out our pricing or contact our sales team.
What about previous TabPFN releases?
Prior versions remain available under their original licenses. TabPFN-2.5, TabPFN-2.6, TabPFN-3 and TabPFN-3.5 have non-commercial model licenses.