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From 15 September until 29 September 2026, all API and MCP users get a 50% reduction from the standard TabPFN-3.5 token rates for the 3.5 family. Standard rates resume on 29 September.
API predictions and Thinking fits consume tokens from the same daily and monthly budgets. There is no separate monthly allowance for the number of Thinking fits. Your usage depends on the computation needed for the model, dataset, and settings you choose.
Usage quotas are separate from per-minute and per-hour request limits. See API rate limits for the default limits on uploads, fits, and predictions.

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Token cost

Tokens measure computation. They are usage units, not a count of words or characters in your data. For TabPFN-3.5, token usage depends on the selected model and the work needed for your request:
  • Dataset size: The training rows, rows to predict, and feature columns all contribute to the workload. Rows contribute more than columns, and column-row interaction also contributes to the end token charge.
  • Number of estimators: n_estimators controls how many model passes contribute to the prediction. More estimators use more tokens and can improve prediction quality.
  • Type of work: Thinking fits and predictions require additional computation. Successful KV cache reuse lowers the prediction charge.
  • Minimum charge: Each billable TabPFN-3 or TabPFN-3.5 operation has a minimum charge of 10,000 tokens to cover request overhead.
TabPFN v2.x charges in proportion to the combined training and prediction rows, the number of columns, and the number of estimators. It uses 8 estimators when not specified, with a minimum charge of 5,000 tokens per prediction request.

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What consumes tokens

The minimum charge still applies to cached predictions. A lower token charge does not change the request rate limits.

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Estimate and monitor usage

With tabpfn-client, estimate a prediction’s token cost before running it. Given your training and test feature tables, X_train and X_test:
The estimate sends only dataset dimensions and your settings; it does not upload the data or consume tokens. Use the same model and estimator settings as your planned prediction. The final charge can differ if the fitted model uses different settings or a cached prediction falls back to standard inference. Check your usage and account limits on the Usage page, or get a usage summary with tabpfn-client:

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Usage pools

Predictions and Thinking fits share a monthly token budget and a daily cap. Every charged operation counts toward both. There is no separate Thinking fit counter. Your account may have custom limits; check the Usage page for the values that apply to you. If a request’s estimated cost exceeds either remaining budget, the API returns HTTP 429 with a message indicating which limit was hit and when it resets. This applies to predictions using existing fitted models as well as new Thinking fits. A warning email is sent when either daily or monthly usage crosses 70% of the limit.

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When tokens are charged

The API reserves the estimated token cost before starting a billable request. This reservation counts toward both budgets while the work is running. Afterward, the charge is adjusted using the actual model settings and execution details, and any unused reservation is returned. For a Thinking fit on a newly uploaded dataset, the initial reservation may use file size until the actual row and column counts are known. The final charge is adjusted once those details are available. If the API confirms that computation did not start, the reservation is refunded. A failed or timed-out request can still consume tokens if work started or its execution status is uncertain.

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HTTP error codes

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Higher limits

Request higher token budgets through the Usage page. Include your expected daily and monthly usage and the types of workloads you plan to run.

API rate limits

Per-minute and per-hour limits for uploads, fits, and predictions.

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.