Nerf is real, i think we initially get full precision models and later quants. My own logs show it clearly for opus 4.5 to 5, consistently a few months post launch, models start making quant based mistakes, like slipping in inappropriate tokens (e.g. chinese ones in english text) which doesnt happen at all in the first few months and regularly later. Additionally frontier problems previously done well start being done poorly, until later model variants where performance mostly holds, likely due to them training on your data reguardless of what boxes you tick.

My local models don't display that degradation, sensed or measured. They consistently perform equally to what I expect of them, precisely because they don't change.

How does twitter explain that? Is my internal model for expectation of capacity magically not drifting for local models but somehow is for anthropic api call based models?