Most "Model X > Model Y" takes on HN these days (and everywhere) seem based on an hour of unscientific manual prompting. Are we actually running rigorous, version-controlled evals, or just making architectural decisions based on whether a model nailed a regex on the first try this morning?
It's evident in the fact that every time AI has historically met certain thresholds (chess-playing, the Turing Test, fluent language), we play with them a little more and find out there's something still lacking.
If questions are fixed they are trivial to game.
Evals are broken - OpenAI showed that SWE Bench Verified was in the training data - models were able to reconstruct the changes from memory (https://openai.com/index/why-we-no-longer-evaluate-swe-bench...)
However, this doesn't mean we should completely give up on benchmarking. In fact, as models get more intelligent, and we give them more autonomy, I believe that tracking agent alignment to your coding standards becomes even more important.
What I've been exploring is making a benchmark that is unique per-repo - answering the question of how does the coding agent perform in my repo doing my tasks with my context. No longer do we have to trust general benchmarks.
Of course there will still be difficulties and limitations, but it's a step towards giving devs more information about agent performance, and allowing them to use that information to tweak and optimize the agent further
Evals are bad because people learn and fit to them. So we do extremely small evals instead.
So there is a definite advantage to never publicizing your internal benchmark. But then, no one else can replicate your findings. You should assume that the space of benchmarks that are actually decent at evaluating model performance is much larger and most of the good ones, the ones that were costliest to produce, are hidden, and might not even correspond very well with the public ones. And that the public expensive benchmarks are selective and have a bias towards marketing purposes.