Add to that the constant need to keep the "alignment"/guardrails/safety/etc. (by which I mostly mean not getting slammed on copyright) which has been demonstrated to bloat "system prompt"-style stuff which is going to further distort outcomes with every turn of the crank and it's almost impossible to imagine how a company could have a given model series do anything other than decay in perceptual performance starting at GA.
"Proving" the amount of degradation is give or take "impossible" for people outside these organizations and I imagine no mean feat even internally because of the basic methodological failure that makes the entire LLM era to date a false start: we have abandoned (at least for now) the scientific method and the machine learning paradigm that has produced all of the amazing results in the "Deep Learning" era: robustly held-out test/validation/etc. sets. This is the deep underlying reason for everything from the PR blitz to brand "the only thing a GPT does" as being either "factual"/"faithful" or "hallucination" when in reality hallucination is all GPT output, some is useful (Nick Frost at Cohere speaks eloquently to this). Without a way to benchmark model performance on data sets that are cryptographically demonstrated not to occur in the training set? It's "train on test and vibe check", which actually works really well for e.g. an image generation diffuser among other things. There is interesting work from e.g. Galileo on using BERT-style models to create some generator/discriminator gap and I think that direction is promising (mostly because Karpathy talks about stuff like that and I believe everything he says). There's other interesting stuff: the Lynx LLaMa tune, the ContextualAI GritLM stuff, and I'm sure a bunch of things I don't know about.
I've been a strident critic of these companies, it's no secret that I think these business models are ruinous for society and that the people running them have with alarming prevalence seriously fascist worldviews, but the hackers who build and operate these infrastructures have one of the hardest jobs in technology and I don't envy the nightmare of a Rubik's Cube that keeping the lights on while burning an ocean of cash every single second: that's some serious engineering and a data science problem that would give anyone a migraine, and the people who do that stuff are fucking amazing at their jobs.