I've run out of quota on my Pro plan so many times in the past 2-3 weeks. This seems to be a recent occurrence. And I'm not even that active. Just one project, execute in Plan > Develop > Test mode, just one terminal. That's it. I keep getting a quota reset every few hours.
What's happening @Anthropic ?? Anybody here who can answer??
It's the most commented issue on their GitHub and it's basically ignored by Anthropic. Title mentions Max, but commenters report it for other plans too.
lol
This fixed subscription plan with some hardly specified quotas looks like they want to extract extra money from these users who pay $200 and don't use that value, at the same time preventing other users from going over $200. Like I understand that it might work at scale, but just feels a bit not fair to everyone?
That's limited to accessing the models through code/desktop/mobile.
And while I'm also using their subscriptions because of the cost savings vs direct access, having the subscription be considerably cheaper than the usage billing rings all sorts of alarm bells that it won't last.
If you look at tool calls like MCP and what not you can see it gets ridiculous. Even though it's small for example calling pal MCP from the prompt is still burning tokens afaik. This is "nobody's" fault in this case really but you can see how the incentives are and we all need to think how to make this entire space more usable.
API request method might have no cap, but they do cap Claude Code even on Max licenses, so easier to throttle as well if needed to control costs. Seems straightforward to me at any rate. Kinda like reserved instance vs. spot pricing models?
I've been using CC until I run out of credits and then switch to Cursor (my employer pays for both). I prefer Claude but I never hit any limits in Cursor.
Quota's basically a count of tokens, so if a new CC session starts with that relatively full, that could explain what's going on. Also, what language is this project in? If it's something noisy that uses up many tokens fast, even if you're using agents to preserve the context window in the main CC, those tokens still count against your quota so you'd still be hitting it awkwardly fast.
they get to see (if not opted-out) your context, idea, source code, etc. and in return you give them $220 and they give you back "out of tokens"
It's also a way to improve performance on the things their customers care about. I'm not paying Anthropic more than I do for car insurance every month because I want to pinch ~~pennies~~ tokens, I do it because I can finally offload a ton of tedious work on Opus 4.5 without hand holding it and reviewing every line.
The subscription is already such a great value over paying by the token, they've got plenty of space to find the right balance.
I've done RL training on small local models, and there's a strong correlation between length of response and accuracy. The more they churn tokens, the better the end result gets.
I actually think that the hyper-scalers would prefer to serve shorter answers. A token generated at 1k ctx length is cheaper to serve than one at 10k context, and way way cheaper than one at 100k context.
i'd need to see real numbers. I can trigger a thinking model to generate hundreds of tokens and return a 3 word response (however many tokens that is), or switch to a non-thinking model of the same family that just gives the same result. I don't necessarily doubt your experience, i just haven't had that experience tuning SD, for example; which is also xformer based
I'm sure there's some math reason why longer context = more accuracy; but is that intrinsic to transformer-based LLMs? that is, per your thought that the 'scalers want shorter responses, do you think they are expending more effort to get shorter, equivalent accuracy responses; or, are they trying to find some other architecture or whatever to overcome the "limitations" of the current?
We're about to get Claude Code for work and I'm sad about it. There are more efficient ways to do the job.
OpenCode is incentivized to make a good product that uses your token budget efficiently since it allows you to seamlessly switch between different models.
Anthropic as a model provider on the other hand, is incentivized to exhaust your token budget to keep you hooked. You'll be forced to wait when your usage limits are reached, or pay up for a higher plan if you can't wait to get your fix.
CC, specifically Opus 4.5, is an incredible tool, but Anthropic is handling its distribution the way a drug dealer would.
Controlling the coding tool absolutely is a major asset, and will be an even greater asset as the improvements in each model iteration makes it matter less which specific model you're using.
Which was nothing new itself of course. Conflicts of interest didn't begin with computers, or probably even writing.
They need more training data, and with people moving on to OpenCode/Codex, they wanna extract as much data from their current users as possible.
(And once you've done that, also consider whether a given task can be achieved with a dumber model - I've had good luck switching some of my sub-agents to Haiku).
by default?
Waiting for Anthropic to somehow blame this on users again. "We investigated, turns out the reason was users used it too much".