Sorry for the delay.
It can be subjective, at this stage of product availability.
Personally, I hate to be frustrated by gross intellectual faults, so I did some research in the past about the best benchmarks to assess pure (simulated, apparent) intelligence. (The quality of the found benchmarks may not reflect what the models seem to do in practice, so one's experience should be compared to the raw numbers out of the benchmarks.) Good ideas emerge in the field: it was proposed and discussed on these very pages that the LLM should be able to solve "murder mysteries", for example (alongside the pattern recognition problems in which IQ tests consist, etc.).
Moreover, the LLM shall not delirate. It is an intrinsic issue with the current architectures (they do not mirror the "Foundational theory of Knowledge", which requires confidence values and relations of foundation between notions), but it is a problem with more or less presence per model. Artificial Analysis has introduced a metric for that.
Moreover again, I want an output style that works well for the purpose - must not be a clashing style like "youngspeak" ("like, awsome") or "paternalistspeak" ("when a planet likes another very much they are attracted...") or "sycophantspeak" ("your question is so deep and interesting") or "wetspeak" ("you can do it, feel this not that")... So, for example, I very much preferred Kimi k2 to gpt-oss-120b. I doubt there are benchmarks for this - "seriousspeak", "maturespeak" - but there should be.