I've heard lots of people say that, but no objective reproducible benchmarks confirm such a thing happening often. Could this simply be a case of novelty/excitement for a new model fading away as you learn more about its shortcomings?
When a new model comes out I test the waters a bit with some more ambitious queries and get impressed when it can handle them reasonably well. Over time I take it for granted and then just expect it to be able to handle ever more complex queries and get dissappointed when I hit a new limit.
Anecdotally, it's quite clear that some models are throttled during the day (eg Claude sometimes falls back to "concise mode" - with and without a warning on the app).
You can tell if you're using Windsurf/Cursor too - there are times of the day where the models constantly fail to do tool calling, and other times they "just work" (for the same query).
Finally, there's cases where it was confirmed by the company, like Gpt-4o's sycopanth tirade that very clearly impacted its output (https://openai.com/index/sycophancy-in-gpt-4o/)
Trusting these LLM providers today is as risky as trusting Facebook as a platform, when they were pushing their “opensocial” stuff
You've also made the mistake of conflating what's served via API platforms which are meant to be stable, and frontends which have no stability guarantees, and are very much iterated on in terms of the underlying model and system prompts. The GPT-4o sycophancy debacle was only on the specific model that's served via the ChatGPT frontend and never impacted the stable snapshots on the API.
I have never seen any sort of compelling evidence that any of the large labs tinkers with their stable, versioned model releases that are served via their API platforms.
> At the time of writing, there are two major versions available for GPT-4 and GPT-3.5 through OpenAI’s API, one snapshotted in March 2023 and another in June 2023.
openaichat/gpt-3.5-turbo-0301 vs openaichat/gpt-3.5-turbo-0613, openaichat/gpt-4-0314 vs openaichat/gpt-4-0613. Two _distinct_ versions of the model, and not the _same_ model over time like how people like to complain that a model gets "nerfed" over time.
Which is why the base model wouldn't necessarily show differences when you benchmarked them.