Unless you're looking at something like a pass@100 benchmark, the benchmarks are confounded heavily by a likelihood of a "golden path" retrieval within their capabilities. This is on top of uncertainties like how well your task within a domain maps to the relevant test sets, as well as factors like context fullness and context complexity (heavy list of relevant complex instructions can weigh on capabilities in different ways than e.g. having a history where there's prior unrelated tasks still in context).

The best tests are your own custom personal-task-relevant standardized tests (which the best models can't saturate, so aiming for less than 70% pass rate in the best case).

All this is to say that most people are not doing the latter and their vibes are heavily confounded to the point of being mostly meaningless.

>just performance is highly task-dependent and even variable within tasks. People get objectively different experiences, and assume one or another is better, but it's basically random.

You are right that this is not exactly subjectivity, but I think for most people it feels like it. We don't have good benchmarks (imo), we read a lot about other people's experiences, and we have our own. I think certain models are going to be objectively better at certain tasks, it's just our ability to know which currently is impaired.

Staying power is probably the most important factor, which is why I'm thinking Google eventually takes the crown.

Anthropic limiting Claude subs to Claude code is what pushed me away in the end because I wanted to keep using Pi.

Can you enumerate why?