Contrary to people here who feel the price increases, reduction of subscription limits etc are the result of the Anthropic models being more expensive to run than the API & subscription revenue they generate I have a theory that Anthropic has been in the enshittification & rent seeking phase for a while in which they will attempt to extract as much money out of existing users as possible.

Commercial inference providers serve Chinese models of comparable quality at 0.1x-0.25x. I think Anthropic realised that the game is up and they will not be able to hold the lead in quality forever so it's best to switch to value extraction whilst that lead is still somewhat there.

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CharlesW 6 hours ago | parent [-]

> Commercial inference providers serve Chinese models of comparable quality…

"Comparable" is doing some heavy lifting there. Comparable to Anthropic models in 1H'25, maybe.

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omega3 6 hours ago | parent [-]

Benchmarks suggests they are comparable: https://artificialanalysis.ai/?models=claude-opus-4-6-adapti...

But let's say for the sake of discussion Opus is much better - still doesn't justify the price disparity especially when considering that other models are provided by commercial inference providers and anthropics is inhouse.

	▲	cbg0 5 hours ago \| parent \| next [-]
		Try doing real work with them, it's night and day difference especially for systems programming. The non-frontier models to a lot of benchmaxxing to look good.
	▲	xienze 6 hours ago \| parent \| prev [-]
		> Benchmarks suggests they are comparable The problem here is people think AI benchmarks are analogous to say, CPU performance benchmarks. They're not: * You can't control all the variables, only one (the prompt). * The outputs, BY DESIGN, can fluctuate wildly for no apparent reason (i.e., first run, utter failure, second run, success). * The biggest point, once a benchmark is known, future iterations of the model will be trained on it. Trying to objectively measure model performance is a fool's errand.