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.

Benchmarks are meaningless. Try it on your own problems and see if it has improved for what you want to use it for.

Benchmark results don’t directly translate to actual real world improvement. So we might guess it’s somewhat better but hard to say exactly in what way

11% further along the particular bell curve of SWE-bench. Not really easy to extrapolate to real world, especially given that eg the Chinese models tend to heavily train on the benchmarks. But a 10% bump with the same model should equate to “feels noticeably smarter”.

A more quantifiable eval would be METR’s task time - it’s the duration of tasks that the model can complete on average 50% of the time, we’ll have to wait to see where 4.7 lands on this one.