However, I believe that runtime model quantization is possible with some publicly-available inference engines (e.g. vLLM), so its not beyond belief that the closed labs do quantize at runtime, either to allocate compute, or to nudge users towards a preferred model (e.g. make the incumbent model dumber to push people to use the latest-and-greatest model, or vice versa to ease the load on the latest model, which is typically larger than the old one).

They can limit how hard the model thinks for a given effort. Suddenly xhigh only thinks as hard as high did, and high shifts down to medium effort, and so on.

They can also serve quantized models. And this has the benefit of practically not showing up in benchmarks at all even if the user experience is obviously degraded.

The other major thing the labs do is silently drop the usage limits. This has become very noticeable for codex users who are suddenly burning through their weekly usage in a few hours.

I have no reason to doubt the claims of the employees at OpenAI and Anthropic who have told us personally multiple times, including here on HN, that they do not degrade the models in order to reduce load.

As for the endlessly long analysis in the OP, it appears it's based on analyzing their random usage data rather than any fixed benchmark. I don't think it makes much sense.