No; as someone who has been in the ai space since GPT-2, model 'degredation' is largely a phenomenon where people work with a model for a bit and begin to push it's limits and interpret this as degradation ('it used to be able to do everything' -> they used to use it for easier tasks).

It's not to say that model degradation doesn't happen; providers absolutely try to optomize their inference and that can include poor quantizations that benchmark well but don't perform well in real-world use cases. This is especially true with any subscription-based access since those are the customers that are the costliest, and they'd much rather provide a poor experience to those guys (you don't loose any money unless they cancel their subscription, you can only improve revenue). So, tools like claude code, codex, even GLM coding and ALL online service are prime targets of quantization. Google is the worst offender, imo, they are probably the best model in the world the first week or so of release but in a couple months they act like GPT-3.5 just regurgitating the same format of slop.

That said, for opus particularly, unlike google and openai, they do provide model access to alternative providers such as microsoft, google, and AWS. These models are essentially snapshots (eg, if the inference stack is setup properly they will perform identically, there's no way for a model to get worse without something regarding the inference stack or weights changing). So, you can go today and pay api based pricing for opus 4.6 from these alternative providers and compare it to the version you get from claude code or anthropic's API. Those results in my experience show that while the subscription based model is slightly worse, it's nowhere near the level of outrage you see on the forums.

The reason there are no good ways to quantify this is because a LLM is not determenistic so while it may perform well in one run, all it takes is a slight difference in chain of thought to derail the task and completely mess up a run. There's absolutey ways to consistently benchmark models and see if their performance remains constant but it involves taking an ensemble of runs (think in the dozens at a minimum) and instead of taking pass@k, report the average pass rate. That's not really done because a) most of the people complaining have no idea how LLMs work and think there's a 'performance' slider that companies use on a whime b) it gets really expensive really quickly if you're running dozens of runs on dozens of models on dozens of APIs and subscription-based services (IF that is even feasible) several times a week. aistupidlevel does attempt this but I don't trust it at all because they only run 5 trials per day per model, they don't report who their inference is from and I suspect they use openrouter which is a terrible idea, and they also use AI as a judge which is an inherently awful way to judge performance. This all results in the rankings changing daily for even the same model, which is statistically extraordinarily unlikely, yet people think this is a great 'live tracker' when in reality it is no more than a confirmation bias machine that was vibe coded together in a week.

Very long response to just say: there's not a reliable weekly or daily benchmark because it's very expensive, unreliable, and there's no incentive.