A model or new model version X is released, everyone is really impressed.
3 months later, "Did they nerf X?"
It's been this way since the original chatGPT release.
The answer is typically no, it's just your expectations have risen. What was previously mind-blowing improvement is now expected, and any mis-steps feel amplified.
What we need is an open and independent way of testing LLMs and stricter regulation on the disclosure of a product change when it is paid under a subscription or prepaid plan.
I mean, that's part of the problem: as far as I know, no claim of "this model has gotten worse since release!" has ever been validated by benchmarks. Obviously benchmarking models is an extremely hard problem, and you can try and make the case that the regressions aren't being captured by the benchmarks somehow, but until we have a repeatable benchmark which shows the regression, none of these companies are going to give you a refund based on your vibes.
https://www.anthropic.com/engineering/a-postmortem-of-three-...
We've got a lot of available benchmarks & modifying at least some of those benchmarks doesn't seem particularly difficult: https://arc.markbarney.net/re-arc
To reduce cost & maintain credibility, we could have the benchmarks run through a public CI system.
What am I missing here?
Unfortunately, it's paywalled most of the historical data since I last looked at it, but interesting that opus has dipped below sonnet on overall performance.
This is not the same thing as a "omg vibes are off", it's reproducible, I am using the same prompts and files, and getting way worse results than any other model.
It has a habit of trusting documentation over the actual code itself, causing no end of trouble.
Check your claude.md files (both local and ~user ) too, there could be something lurking there.
Or maybe it has horribly regressed, but that hasn't been my experience, certainly not back to Sonnet levels of needing constant babysitting.
If LLMs have a 90% chance of working, there will be some who have only success and some who have only failure.
People are really failing to understand the probabilistic nature of all of this.
"You have a radically different experience with the same model" is perfectly possible with less than hundreds of thousands of interactions, even when you both interact in comparable ways.