TabPFN and TabICL vs. tuned XGBoost: the model that doesn't train won 14/14
I measured it on fourteen datasets from the Grinsztajn benchmark, with the same split and the same clock for everyone. The one that does not train wins, the advantage holds up to 32,000 rows instead of breaking, and the most-cited model can no longer be downloaded without an account. If that is true, half a decade of practice changes shape.
- ▪I measured it on fourteen datasets from the Grinsztajn benchmark, with the same split and the same clock for everyone.
- ▪The one that does not train wins, the advantage holds up to 32,000 rows instead of breaking, and the most-cited model can no longer be downloaded without an account.
- ▪If that is true, half a decade of practice changes shape.
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| Original publisher | Efrain Garay |
| Canonical URL | https://efraingaray.com/en/blog/tabpfn-vs-xgboost/ |
| Publication time | Mon, 28 Sep 2026 02:27:40 +0000 |
| Retrieval time | 2026-09-28T04:46:10.304Z |
| Last seen | 2026-09-28T04:46:10.304Z |
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Home/Blog/TabPFN and TabICL against tuned XGBoost: the model that does not train won on fourteen tables out of fourteenModelsBenchmarksGPUDataTabPFN and TabICL against tuned XGBoost: the model that does not train won on fourteen tables out of fourteenThe claim behind TabPFN and TabICL is that they predict on a table without ever training on it and still beat tuned boosting. I measured it on fourteen datasets from the Grinsztajn benchmark, with the same split and the same clock for everyone. The one that does not train wins, the advantage holds up to 32,000 rows instead of breaking, and the most-cited model can no longer be downloaded without an account.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Efrain Garay.
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