this post was submitted on 16 Jun 2026
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The actual cost to OpenAI is likely much less. The number in the article is calculating the API cost that a fully maxed out subscription would incur theoretically. The API token cost, however, is far above the actual computational cost.
I disagree - the analysis takes as a basis a very, very generous margin of 75% on API prices. There is no way they have that much of a margin, this is wishful thinking.
And every single user who maxes out their 200$-subscription burns more cash than they take in from 70 subscriptions that lie dormant.
I was talking to one of our cloud architects at work yesterday. They did a test and just ran in "asdf" to a chat prompt, and were able to trace the costs. It was 12 cents.
I could totally see AI costs getting out of control very quickly. Doing something like a Copilot formula in an Excel spreadsheet is easily going to run up hundreds of dollars of costs eventually.
It's a 200 dollar subscribtion. Are any actual users around that can provide info on how actively they are using it? I would feel that at 200 dollars they give you loads of headroom.
For Anthropic at least, your usage is broken up into five hour windows. During peak periods the usage is burnt in like 1/3 of the time compared to off-peak. You can do heaps, like build large sophisticated applications with 100% agentic workflows, if you spread your usage out over your five hour windows and especially if you use it mostly in the off peak.
On pro, your five hour usage is basically one solid feature developed or one big refactor/cleanup with opus 4.8 with some room left over for reviews, planning and a little mistake. 5x and 10x plans are more in the price range you mention, so multiply that as such. Or you'd get a combination of general purpose daily usage, and development usage.
There's also a weekly cap but I haven't hit it.
Fable, aka locked down mythos, when it was available on pro could complete my entire todo list for the day in half an hour at astounding quality while simplifying everything it touches, finding and fixing preexisting vulnerabilities in code review and finishing with 98% of the five hour quota used off-peak
I don't think most hobbyists would use more than a quarter of a pro plan due to the five hour lockout mechanism
https://techcrunch.com/2026/06/02/uber-caps-employee-ai-spending-after-blowing-through-budget-in-four-months/
This isn't that relevant to my question.
The actual price is hard to really know, but I think training should also factor in. The hype of LLMs is based on the fantastical idea of continunous improvement forever, so you need to keep training. Even ignoring the hype part, you still need to retrain simply to update the data inside the LLM.
I guess we'll only know for sure after the crash/readjustment.