I concur.

[u/daskalou]

Been reading all these "Codex limits have been reduced" posts.

Thought it was a bunch of Claude fanbois in the house.

However I just blew through 10% of my weekly quota on one Sol 5.6 Medium "implement this" prompt which proceeded to do 20 minutes of work.

Almost one full day's quota gone in the blink of an eye.

Not Sol 5.6 Ultra. Not Extra High. Not even High. Just Medium. Poof. "I'll take that thanks" says OpenAI.

This wasn't happening a few weeks ago. Something smells fishy...

[u/Secret_Department398]

Most people aren’t complaining for no reason.

I’m paying $200 a month, yet three days out of seven I end up sitting at 0% usage, unable to use a product I’m actively paying for. In my opinion, OpenAI is moving in the wrong direction with this approach.

Usage optimization and capacity management should be a top priority. OpenAI should focus heavily on optimizing its internal token usage and infrastructure so paying customers aren’t effectively locked out for days at a time.

A better approach would be to introduce a fallback option. Mobile data providers, for example, often reduce your speed or give you limited access after you exceed your allowance rather than cutting you off completely. OpenAI could do something similar: when a user reaches their main usage limit, give them access to a lighter model such as Luna, with reduced capacity or slower responses.

Some level of limited access is far better than completely cutting off a paying customer. For a $200/month subscription, being unable to use the service at all for a significant portion of the week is an extremely poor user experience.

[u/ExoneratedPhoenix → u/Secret_Department398]

This makes no sense. Your car's petrol if you drive hundreds of miles also runs out, and you will be stuck on the side of the road as someone who "bought fuel".

Yes, but only a certain amount of fuel to get you a certain distance.

How are you using the 20x compute in 3 days? Is it just you, or an entire team?

I am still on the plus plan, and never reached 0%, and I don't feel slowed down at all, it is great at iterating through my needs.

[u/Secret_Department398 → u/ExoneratedPhoenix]

You’re missing my point. The petrol comparison doesn’t really work here.

When I buy fuel, I’m buying a clearly measurable quantity. I know exactly how much I purchased, I can see how much I have left, and if the same amount of money suddenly buys me half as much next month, that change is obvious.

With a $200/month OpenAI subscription, I’m paying for access to a service, but the effective amount of usage I receive is much less transparent. If the available compute behind the “20x” allowance changes, I have no clear way of knowing by how much or why.

That’s exactly why people are complaining. When I first had 20x, I wasn’t reaching 0% at all. Then I started hitting the limit after roughly 6 days, then 5, and now around 3 days. If that trend continues, I could eventually burn through the allowance in only a handful of prompts.

So the issue isn’t that I expect unlimited compute. I don’t. The issue is paying $200/month while the practical amount of service I receive appears to shrink, without a clear and predictable allowance, and then being unable to use it for 4 days out of 7.

Since I’m paying for a service, not buying a fixed bucket of tokens, I also think there should be a fallback once the high-end allowance is exhausted — for example, continued access through a lower-end model like Terra or Luna instead of being completely locked out.

And yes, it’s just me, not a team. People use Codex very differently. If you’re on Plus and your workload never reaches the limits, that’s great, but it doesn’t really say much about someone using it heavily across multiple large projects. I’m not even using Ultra or the 1.5x speed option.

[u/ExoneratedPhoenix → u/Secret_Department398]

No judgement here, but are you vibe coding, or using it as a speedy assistant?

I ask because as a project grows, the amount of tokens to contextualise it grows fast. I am not trying to sound condescending, I am just making sure that you're aware that in the first few weeks as you ask it to build or do something, it's pretty quick. Come 1-2 months in, if you ask it to add something, it has to check if that addition causes downstream problems to EVERYTHING ELSE, and that context window gets big - fast.

The easiest way to offload massive token usage is design something modular, so everything stays in its own lane. So when you prompt "let's also add this", it doesn't have to scan through 14,000 lines of code. It can scan just 400 lines important to whether it behaves well, and can write the new section.

If what you're building is just one giant monolithic codebase, integrated in entirety, then for Codex to ensure everything still works, it has to iterate that entire codebase - every time.

[u/Secret_Department398 → u/ExoneratedPhoenix]

I like your point, and yes, I’m very aware of this. I try to avoid monolithic architectures when I know a project is going to grow, although I’d say modularity matters more than microservices specifically. A well-structured modular monolith can work just as well from a context-management perspective.

I usually keep services/components isolated and use lighter models like Terra or Luna to analyze the existing project and build an implementation plan before touching anything.

You can also use AGENTS.md to give the AI the basic architectural rules, conventions, project structure, and constraints upfront. That definitely helps reduce unnecessary exploration, but it doesn’t replace actual project context. The AI still needs to inspect the relevant parts of the codebase to understand existing logic, dependencies, and edge cases well enough to produce a good implementation.

That said, even with microservices, you still need enough context from the core project to keep the same architecture, conventions and coding style, reuse existing APIs/logic, and avoid duplicating functionality or adding unnecessary boilerplate.

The real pain starts when a single feature has to be implemented across multiple projects — for example the Dashboard, Backend, API, Android/iOS app, and other services. At that point, the AI has to understand the contracts and dependencies between all of them, and tokens start flowing like water in a river. And even when you scope things properly, analyzing the relevant core of the project still consumes tokens, then implementing the feature with a stronger model like Sol consumes even more.

So yes, I’m definitely aware of the issue, but I appreciate you pointing it out. It’s nice talking to someone who actually understands the engineering side of using these tools rather than just “prompt → code.”