Plus I've found that the only time models go above 100k tokens anyway is when they've started looping at which point it's much better to go back anyway.
Anecdotally most models know their recall is terrible (or have been trained to act as such), that's why they constantly reread files before editing or while reasoning.
It seems that people have different workflows or repos, or memories or prompts or expectations.
I read it as a models performance being random and observed differences in the opinions are the results of the overinterpretation of the random outcomes.
I think however that some people seem to be always lucky which indicates that it is not random but rather some fixed differences between people and their environments.
I think that's issue, rather than 60K being small.
Most of the actual edits/changes I request to codex are solved within 100-150K tokens, beyond 200K I'd definitively try to restart the session as soon as I could as all models are horrible once you get across ~20% of the total context size. And this is while working on +million LOC codebases.
Problem I guess is that there is no solid and concrete evidence of this (to me [and others seemingly] obvious) degradation, but should be easy to prove, yet no one has time to sit down and show it :)
But the likelihood of a model getting minor details wrong once you're above some magical threshold between 15-20%, seems to skyrocket, and I hit that issue sufficient amount of times that now my workflow is trying to prevent that.
I routinely get claude to do things pretty decently and finish up easily in the 4-5 digit range of tokens. It seems to be doing the right kind of thing to not waste its time looking at 1000 files.
"YOU'RE HOLDING IT WRONG!"