Token cost
Each prediction request is charged based on the number of rows, columns, and estimators used. TabPFN v3 uses a sublinear cost function — doubling your data does not double the cost. Because TabPFN-3 is fundamentally more efficient at handling larger datasets, large-scale workloads are significantly cheaper than on v2.x. TabPFN v2.x uses a linear cost function:n_estimators defaults to 8 when not specified. The floor of 5,000 tokens applies to every request regardless of size.
You can check your current usage with the get_api_usage() function in tabpfn-client, or monitor it in your account dashboard at ux.priorlabs.ai.
Usage pools
| Pool | Default limit | Reset schedule |
|---|---|---|
| Daily prediction tokens | 50,000,000 | Midnight UTC |
| Monthly prediction tokens | 200,000,000 | 1st of each month, midnight UTC |
| Thinking fits (monthly) | 20 | 1st of each month, midnight UTC |
Thinking fit quota
Thinking mode fits are metered separately from prediction tokens. Each call toPOST /tabpfn/fit with thinking enabled counts against the monthly thinking fit quota, regardless of dataset size or fit duration.
When the thinking fit limit is reached, /tabpfn/fit returns HTTP 429. Prediction requests are unaffected — you can still predict using previously fitted models.
Dataset limits
Limits vary by model version. UseGET /tabpfn/get_model_limits to retrieve the current values for your account.
| Constraint | v3 | v2.6 | v2.5 |
|---|---|---|---|
| Max train rows | 1,000,000 | 100,000 | 50,000 |
| Max train cells (rows × columns) | 100,000,000 | 20,000,000 | 20,000,000 |
| Max test rows | 200,000 | 100,000 | 50,000 |
| Max classes | 160 | 10 | 10 |
| Max columns | 2,000 | 2,000 | 2,000 |
| Max upload size | 5 GB | 5 GB | 5 GB |
output_type="full" is limited to 400 test rows. Use "mean", "median", or "quantiles" for larger test sets.
HTTP error codes
| Status | Condition |
|---|---|
| 429 | Daily, monthly, or thinking fit limit exceeded. The response body includes the specific limit, current usage, and reset time. |
| 401 / 403 | Authentication failure — missing, invalid, or expired token. |
| 422 | Validation error — dataset exceeds model limits, invalid parameters, or upload issues. |
Higher limits
If you need higher token budgets or more thinking fits, contact hello@priorlabs.ai with your projected daily token volume and peak thinking usage.Thinking mode
Configure fit-time optimization and understand thinking parameters.
REST quickstart
Full upload → fit → predict walkthrough.
TabPFN-3 changelog
What’s new in v3 — scale, capabilities, and migration.
Security
Encryption, data isolation, and access controls.