There is no hallucination benchmark currently.

I was researching how to predict hallucinations using the literature (fastowski et al, 2025) (cecere et al, 2025) and the general-ish situation is that there are ways to introspect model certainty levels by probing it from the outside to get the same certainty metric that you _would_ have gotten if the model was trained as a bayesian model, ie, it knows what it knows and it knows what it doesn't know.

This significantly improves claim-level false-positive rates (which is measured with the AUARC metric, ie, abstention rates; ie have the model shut up when it is actually uncertain).

This would be great to include as a metric in benchmarks because right now the benchmark just says "it solves x% of benchmarks", whereas the real question real-world developers care about is "it solves x% of benchmarks *reliably*" AND "It creates false positives on y% of the time".

So the answer to your question, we don't know. It might be a cherry picked result, it might be fewer hallucinations (better metacognition) it might be capability to solve more difficult problems (better intelligence).

The benchmarks don't make this explicit.