Model profile

TabPFN 3.5

TabPFN 3.5, the latest version by Prior Labs, is pretrained on synthetic tables and uses labelled examples as context for new predictions. A single shared checkpoint handles both classification and regression, rather than using separate pretrained models for the two tasks.

Tabular FoundationRegular feature limit: 20,000Source ↗

Model profile

TabPFN 3.5

Default parameters. Peer models use their prespecified benchmark-default configuration without per-dataset tuning. This card describes that configuration, not the best achievable tuned model.

Operating map

Macro-F1 Elo relative to Random Forest. Columns are training-sample caps; rows are dataset modalities. Small labels show the number of binding targets.

Elo difference vs. Random Forest

Fixed nonlinear scale; extremes capped at ±1,000 Elo.

No result / comparison unavailable

Reliability

Failure rate map

Dataset modality

Failed fits / (successful + failed fits) for the selected model and modality. Design skips are omitted. A dash means no attempted fits; 0% means all attempted fits succeeded. Hover for counts. Colours span 0–20%, with rates above 20% using the darkest shade; cell labels and hover retain the actual rate. This chart is independent of the performance sensitivity view.

Reference benchmark results

Strict Macro-F1 Elo at p=10,000, n=100. Random Forest is anchored at 1,000; intervals are target-bootstrap 95% intervals. These reference results stay fixed while you explore other budgets in the charts. Overlapping intervals do not establish a clear winner.

Dataset modality	Elo	95% interval	Targets
All modalities	1,197	1,154–1,242	44
DNA methylation	1,176	1,086–1,342	4
GLM/PLM Embeddings	1,237	1,009–1,543	4
Gene expression	1,338	1,256–1,441	10
Genomic prediction	1,003	841–1,160	4
Metagenomics	1,149	1,107–1,194	10
Molecular properties	1,185	1,122–1,257	10
Other	1,492	1,492–1,582	2