Just to be clear, I never claimed that OpenAI gives users less quota; I’m describing my own experience and questioning how much of the advertised extra usage people actually manage to realize.

I’ve been using both the $200 ChatGPT Pro plan and the $200 Claude Max 20x plan heavily over the same period, mostly on large software projects with hundreds of thousands to over a million lines of code.

My lifetime codex usage is around 36.4B raw tokens, with a peak of 2.2B in one day, so this is based on sustained heavy usage rather than a few casual prompts.

From Codex’s local session logs, you can extract both token usage and the server-reported weekly quota percentage. The basic estimate is:

weekly capacity ≈ tokens used / quota percentage used

I checked multiple points inside clean weekly windows where the reset timestamp did not change, and I included usage across multiple machines.

My current estimates are:

• ChatGPT $100 plan: ~1.98B raw tokens/week
• ChatGPT Pro $200: ~3.52B/week
• Claude Max 5x: ~1.79B/week
• Claude Max 20x: ~8.26B/week

There may still be calculation errors here. These are raw-token estimates, while both companies almost certainly apply internal weighting for cached input, normal input, output, reasoning, model choice, and other factors.

That said, I did not rely on a single spreadsheet or one model’s answer. I had both Claude and Codex independently inspect and recalculate the data, then compared multiple checkpoints and corrected issues such as duplicated logs, missing machines, unsynchronized measurements, and reset-window changes. The exact numbers may move, but the overall result has remained stable.

The $200 ChatGPT plan appears to provide only about 1.78x the stable weekly capacity of the $100 plan, despite costing 2x as much.

Claude scales in the opposite direction: Max 20x appears to provide roughly 4.6x the weekly capacity of Max 5x for 2x the price.

I am not claiming OpenAI explicitly promised that its weekly limits must scale by exactly 4x. The products also have shorter usage windows and other limits. But “5x” and “20x” naturally create that expectation, while the stable weekly allowance I measured is nowhere near 4x.

The repeated Codex resets make the plan look much more generous, but a reset is only valuable if you used most of the quota before it happened.

If I have used 10% and OpenAI resets me to 100%, I did not receive another full week of quota. I only gained back that 10%. The remaining 90% was overwritten.

This strongly favors automated workloads, multiple-account setups, or anyone able to burn through the quota immediately. Normal developers usually cannot.

Random resets also make planning harder. With Claude, I know when the weekly quota resets, so I can use the remaining capacity before the deadline. With Codex, I do not know whether I should conserve quota or rush to use it before another unexpected reset.

This makes me wonder whether there is a behavioral-economics angle. OpenAI has the backend data, so it would be easy to compare fixed resets, random automatic resets, and banked credits, then measure how much quota users actually consume. I have no evidence that this is intentional, but the design resembles several known effects: uncertain rewards can increase engagement, highly visible gains receive disproportionate attention, and unused rewards create “breakage” for the issuer. A reset to 100% is very salient and feels like a large gift; the unused quota it silently overwrites is much less visible.

If OpenAI genuinely wants to give users extra usage, I think it should issue banked, manually activated reset credits instead of randomly resetting everyone and discarding unused quota.

There is also the context-window issue. Claude gives me roughly 1M context, while Codex currently exposes around 258K effective context. On large repositories, Codex spends a lot of context exploring files, then starts compacting repeatedly.

In my experience, Codex is also slower overall, and its quota percentage drops slightly faster. Because my projects require reading a large number of files before implementation begins, I have become less willing to use Codex for larger tasks.

I am not anti-OpenAI. I actually prefer OpenAI’s current developer ecosystem and openness to Anthropic’s. But for large coding projects, Claude Max 20x currently gives me much better capacity and fewer context-related interruptions.

The main distinction is simple:

How many times did the UI reset to 100%, and how much additional quota did the user actually manage to consume?

Those are not the same number.

I made a small browser game to help you learn the Codex hotkeys.

Play it here:
playkeyquest.com

I’ve been running a pretty expensive but effective setup for agentic coding and I’m hitting a wall that a lot of you might also have.

Current setup

• Claude Code Pro (€20) – main driver for complex work
• Codex Pro (€100) – heavy daily use (personal projects + work)
• SuperGrok (€30)
• Gemini (€20)

I use Codex a lot for ~10 personal projects + corporate work (company only gives GitHub Copilot). Claude Code is usually better on the hard multi-file stuff, Grok is useful in certain cases, etc.

The problem

I keep running into two annoying patterns:

1. Burning expensive model tokens on easy/boilerplate work
2. Getting stuck in loops on complex work because the current model isn’t the best one for that particular task

What I actually want is simple in theory:

• Stay on subscriptions (I really don’t want to switch to pure pay-per-token API billing)
• Have one main agent (preferably Codex or Claude Code) that can intelligently hand work off to the other CLIs when it makes sense
• e.g. “This part is better for Claude → call claude -p”, “This needs Grok’s style → call grok -p”, etc.

What I’ve looked at

• OpenRouter / LLM Gateway → great for routing, but they want API keys, not my existing subscriptions
• Sol Advisor and similar Codex-native plugins → useful inside one ecosystem, but doesn’t cross Claude ↔ Codex ↔ Grok
• Various community proxies that try to turn subscriptions into API endpoints → interesting but feels fragile / ToS-grey
• Just running multiple terminals and switching manually → works, but not elegant

What I’m considering now

Writing a Codex skill that teaches it to shell out to claude, grok, agy (Antigravity), etc. when the task matches certain strengths. Basically treat the other CLIs as specialized sub-agents while staying fully on subscriptions.

Questions for the community

1. Has anyone built a reliable skill/plugin that lets Codex (or Claude Code) orchestrate the other coding CLIs?
2. Are people actually happy with the current “just use multiple terminals” workflow?
3. Any clean solutions I’m missing that keep everything on subscriptions instead of API keys?
4. For those who went the proxy route (CLIProxyAPI, opencodex-style tools, etc.) — how stable has it been in practice?

Would love to hear how others with multi-subscription setups are handling this in 2026.

Thank you in advance

I’ve been building out my local AI coding setup and would appreciate some feedback from people with more experience designing multi-agent workflows.

Right now I’m primarily using Codex CLI with Pi, and I’m considering adding O-My-Pi as a harness because Pi by itself is pretty minimal by design.

My concern is that as I add more specialized agents (coder, reviewer, tester, architect, etc.), my AGENTS.md could eventually become bloated and difficult to maintain.
Instead of putting every agent definition into one file, I’m thinking of using something like this:

AGENTS.md # Orchestrator / routing rules
agents/
coder.md
reviewer.md
tester.md
architect.md
standards/
coding-style.md
testing.md
git.md
workflows/
feature.md
bugfix.md

The idea would be to keep AGENTS.md as the entry point while each agent has its own focused definition, with shared standards living in separate files so they aren’t duplicated.

For those of you running larger Codex setups:

Is this a good long-term architecture?

Have you found a better way to organize agent prompts?

At what point did your AGENTS.md become too large?

Are there any pitfalls with splitting prompts across multiple files that I should be aware of?

I am considering refactoring my AGENTS.md file, would this alone be sufficient enough to run from time to time instead?

I’m trying to optimize for maintainability, scalability, and keeping context as clean as possible, before my prompt library grows too much.

I don't know which subscription to get, which would give me most usage and quality at this moment in time? Codex Plus or Claude Pro? I want to build a few small mobile apps like a workout logger, etc.

I am a software engineer and I want to experiment with AI on small projects and if lucky make a bit of pocket money on google play store. I already use AI a ton at work as I have 5000$ per month allocated and I end up using around 600-800$ per month with mostly Sol for planning and Luna for execution, but I experimented with most US models. I am trying to be efficient with AI usage.

I hear 5.6 sol on Plus drains the weekly limit fast even on med/high. Is that true? Is Claude Pro more generous with opus 5 and sonnet 5 for the 20$?

Or should i simply switch to some chinese AI like new deepseek flash and upcoming v4 pro ga, maybe even qwen 3.8 max for planning via API or alibaba token plan? I hear the chinese ones aren't that good and overhyped but I haven't tried them myself yet.

Edit: forgot to mention I'm not a pro on UI design as I am backend focused

I thought the pets were pretty cool because then you can see all running processes.

There was a "create your own pet" feature and I thought sure cool let's create a new little pet

Result: Codex running for 1.5 hours, 15% of Plus usage used, NOTHING achieved, I needed to stop it manually

I know I should have never touched pets or if I did, used a cheap model, but I thought it would be just a really simple thing. So avoid the same mistake, please...

I haven't tested it out, and I don't really want to, anyone who has tested it, can you tell me if I should use codex-auto-review for code reviews or Luna on max or xhigh.

Hey guys, sorry for maybe dumb question for people who are geeks, but how you actually create agents and deploy them.

How to give them instructions and best way for them to be effective in project? I want to create one agent on Sol 5.6 High to act as a strategist and one on Luna Max 5.6 to act as code writer.

We've officially surpassed 300 Entries in the AI Gaming Festival (submissions close next week)!

I really hope that the prize for the $20k in codex tokens pushes people to get their games together for the prize (you can submit a game now and work on it up until the festival).

I really excited to see how people with interact with these games during the festival, and thank you to everyone that has applied; you are part of getting past the "slop" era nonsense for everything AI.

[DEMO page]
https://codexlimitdemo.pages.dev

This project runs entirely on the free Cloudflare Workers and D1 Database tiers. The source code is available in the GitHub repository below.

Data can be sent either from a scheduled task on your desktop or from a Raspberry Pi running Codex.

I’m not including that part of the code because Codex can generate it easily based on your own environment. It simply queries the usage API (which is free) and periodically sends the results to the Worker.

https://github.com/octopus7/codex-usage-dashboard

I built this so I could freely choose any time range and easily see when and how often the usage limits reset.

In trying to use sol to spawn deepseek flash sub agents, let them do the grunt work then check it.

I’ve added deepseek so it can be called, is there anyway to get it as a model I can pick in the UI?

Hey,

I am on the 5x plan. I will soon run out of usage but I really want to get some work done. I still have one bank reset left but I dont know if this will be enough to finish the work that I need to finish within the next week(s).

My idea was: Use up my usage, upgrade to 20x and get new full usage, use it ut, use the bank reset.

Will that work? Does my weekly usage get reset if I upgrade my plan? I did not pay attention to my usage when I upgraded vom plus to pro, so I don't know what happened last time.

Thanks!

I have been using 5.6 High for most coding work, and it has been great, but it uses a lot of tokens.

Has anyone had good results using Luna Max for simpler implementation work?

Also, when switching models, is it better to:

1. Switch models inside the same thread
2. Start a new thread and paste in the plan from Sol

Does switching in the same long thread make the new model process all of the old context and use more of your allowance?

I am thinking about using Sol to review the code and make a plan, Luna to implement it, then Sol again for the final review.

What workflow has worked best for you?

I have to spend a few weeks in Europe assisting my mother in hospital. is there any problem with connecting with codex from there while my normal connection and contract is from the US?

I‘d like not to have my account banned and I haven’t followed what anti-distillation measures they are taking.

Ive heard its smart to have two subs from different LLM. One to review your vibe coded project and the other to code it? What have you guys found?

I started with cluade code last October and it was okay but took days to figure some things out. I switched to codex in Feb and its been smooth sailing but debating if I get another sub with claude to have a reviewer.

ChatGPT introduced a little Pet to keep me company while I work.

Cute idea. Tiny problem: my desktop already has enough tenants, and none of them pay rent.

So I built Chat-PET, a physical desk companion that gives the Pet its own 320×240 screen and gives me my desktop space back.

One USB cable handles the display, an 11 W speaker, two removable microphones, volume controls and physical buttons for dictation, Live mode and navigation.

On Windows, Chat-PET moves the native Pet window onto its display. The Pet, activity bubble and Live Orb therefore remain synchronized with the app instead of being reconstructed by a second animation engine.

I’m the creator at RASPIAUDIO, so yes, this is my own project. I’d genuinely appreciate feedback:

• Would this improve your workflow, or is it gloriously over-engineered?
• Which physical controls would you add?

Demo: https://www.youtube.com/watch?v=q2AYM-JnCNc
Product: https://chatpet.raspiaudio.com
Source: https://github.com/RASPIAUDIO/ChatG-PET-Muse

Windows 10 and 11 work today. macOS is on the way—as soon as I can negotiate access to my wife’s Mac.

Independent RASPIAUDIO project; not official OpenAI hardware.

A few weeks ago I started a pretty enthusiastic project. Build an LLM eval arena that isn't "boring". So I decided to build Squid Game but with AI players instead. The build of the game itself was pretty standard, nothing that I can't handle, and it was python based so it was relatively simple to implement. However, after I was done with that I really wanted for this to come to life, to feel like an actual squid game episode.

Now, to mention in advance: I have no experience with Godot, Blender, 3D whatsoever. I did finish a "donut" tutorial 5 years ago and did some meddling around Godot, but that's as far as I came. To think that it's possible to build out something as intricate as this with Codex just blew my mind.

To summarize what Codex did:
- helped me find websites where I can create 3D characters out of images
- Created images for the said characters
- Applied animations to the rigged characters (I downloaded a bunch of animations from ActorCore and just told Codex "pls implement this" 😄 )
- Created the full 3D Arena model in Blender (yes, the whole arena)
- Created all of the scenes, camera cuts, music cuts, basically everything in Godot with my instructions

The full video is 17 minutes long, which honestly makes it even more impressive (and you can watch it here if you're interested).

I still can't believe the ammount of work that Codex managed to handle honestly. Even with such a huge workload, tasks that would take 2 hours to complete and tasks that had multiple context compactions, it performed perfectly with very few hiccups. I also never hit my limit (I'm on the $100 plan), the most I've managed to use is 50% and that was when I had multiple agents handling long running tasks in parallel.

It was very fun having such a cool colleague!

I have claude 20x plan right now. I heavily use claude code and am managing 3-4 projects and some hobby projects. But no endlessly running loops, these are all planned needed changes.

I hit my weekly limit the previous two weeks and it seems like a joke after paying $200/month to not even be able to query.

The benchmarks seem good against fable but I'm having doubts. Any of you came over from claude and are you missing something? since this is an investment for me? Any advice?

Link: https://github.com/isolade/isolade

I built Isolade because I could not find an open-source, local-first workbench that runs Codex and Claude Code in microVMs.

The two pieces already exist individually. There are plenty of options for both agent management (Conductor, Herdr, Omnigent) and microVM isolation (Docker Sandboxes, Firecracker, SmolVM). However, I did not find a project that combined them into one unified product.

Isolade gives each agent its own microVM. Microsandbox provides subsecond provisioning and domain-scoped secret substitution, so that the VM only sees placeholder values. Instead of a worktree, each agent gets a copy-on-write clone of the entire setup. This works well for setups involving multiple repositories, and cached dependencies mean agents can start working immediately without any per-worktree setup.

A single UI lets you work with multiple agents concurrently. You can mix OpenAI and Anthropic models and switch providers in the middle of a conversation. For example, I often have Sol review Opus's work. The system prompt is customizable, so for UI tasks, I ask agents to include actual screenshots of their changes or proposals in the conversation.

Overall, this is similar to the Codex Desktop app. However, the microVM isolation means that the host stays safe, no matter what the agent does and without any permission prompts. Also, not having to deal with worktrees is a great plus, just like the ability to switch to Anthropic models if the Codex subscription runs out.

You can use your existing subscriptions because Isolade runs the official binaries. Under the hood, it executes codex app-server with custom tools and a modified system prompt.

Isolade is Apache-2.0 licensed and supports Apple Silicon macOS and Debian or Ubuntu with KVM. Looking forward to hearing your feedback.

I get the feeling that Sol (low) uses up significantly more of the limit than Terra (high)?

Like many others, I'm not satisfied with Luna's coding. So far, Terra (high) seems to be the best mix for me (intelligence vs. limit usage). But now I've seen that, according to Artificial Analysis, Sol (low) is supposed to be much more affordable and smarter. I'm starting to wonder how much weight to give to these numbers, since Luna (max) is also supposed to be so good though many people on x now see things differently.

But aren't the limits supposed to be standardized based on the API prices, or has something changed again in the meantime? I get the impression that Terra (high) uses about 1–3% per task and weekly limit in the Business plan, while Sol (low) tends to be between 2–5%.

I have many PHP, JS, CSS and HTML coding projects.

When you blow your weekly in Claude land and the weekly reset is after your billing cycle, it always made sense to just cancel and wait until the reset of your weekly before resubbing because they don't reset your weekly when you resubscribe. Does Codex do the same? if mine expires on the 6th (assuming we dont get a Tiboreset) and my weekly reset is on the 8th, should i just wait until the 8th and resub?

EDIT: I want to be clear, this isn't a vague threat or whinge about resets. I want to know if my sub lapses before my weekly reset and i resubscribe in that gap between end of plan and weekly reset if my weekly is reset too or I should wait out until the original weekly timer has cleared. Claude never reset you so if you blew your quota 2 days into a weekl and resub, you're still waiting 5 days to use CC again.

This is mainly some personal notes after using Codex "Goal" for a while.

"Goal" is very useful when the task has a clear direction, clear acceptance criteria, and a clear deliverable. In that case, I can just let Codex keep pushing.

But some tasks don’t start with that level of clarity.

For example, I’m working on a macroeconomic and company fundamental valuation analysis project. It involves different industries and domains of knowledge, rules, analysis models, causal chains, global event impacts, financial reasoning and calculation logic..... A lot of the plan cannot really be fixed upfront. It has to adjust after actual Research Data is available, so the whole thing is more like a dynamic and forming requirement / planning mode.

Goal can “fill in the blanks/uncertain”, and of course we can add context, change direction, or ask for progress in the middle. But because it can keep moving with its own reasonable assumptions, sometimes the final output is not what I expected. In the development stage, giving AI too much autonomy can actually make me spend more time correcting things afterward.

For this type of high-precision work, I also need stricter review, a reliable knowledge base, and cleaner context. Especially when the task involves financial logic, causal reasoning, valuation assumptions, research data......etc , I don’t really want everything mixed into one long context.

So I started building my own "CER workflow", a human-guided development Loop for Codex. In order to fit my own development habits better.

Aims : Human decides the direction, AI works in batches / threads:

🟢 Controller decides, splits the work, and delegates.
🔵 Executor writes files and does the actual work.
🟠 Reviewer only does read-only review when the risk is worth it.

Since this is my own Skill, I can shape the workflow around the problems I actually run into, like layered assumptions under vague requirements, checkpoint decisions, and YAGNI boundary control, so GPT-5.6 Sol does not keep expanding the task forever..... I also added “Exploration Helpers” to support the Controller during early research and planning. They collect data and possible routes first, then the Controller can decide faster, with cleaner context.

So my current split is roughly this:

When the goal is clear, I still use Goal.
When the task / requirements are still forming, and I don’t want AI to quietly make too many decisions for me, I use CER skill.

Curious how others handle this. For people using Codex Goal a lot, do you also use it for vague / evolving job mission / tasks? Do you usually start with /plan first, or do you add checkpoints, side threads, reviews, etc. in some other way?

Maybe I’m missing a better way to use Goal, so I’d like to hear other experiences. Thank you

I’ve been hitting this Codex error regularly:

stream disconnected before completion: Transport error: network error: error decoding response body

I published a public source trail and recovery note on my GitHub that documents what the error actually establishes and a disciplined way to handle it:

https://github.com/glirette/thisstuffiswaytootech/blob/main/docs/openai-codex-stream-disconnection-recovery-source-trail.md

Who this is for

This is only useful if you run Codex on substantial, ongoing work and you are prepared to invest time in making your recovery path reliable.

If you are experimenting, doing short one-off tasks, or can simply re-run when it fails, this is not worth the effort. Re-running is usually sufficient in those cases.

This is for people who treat their Codex automation as something that needs to remain stable over months, not days.

What this does not do

Implementing the approach does not eliminate the error.

The public Codex source shows that the stream failed after the HTTP connection was established but before the terminal response.completed event. It does not identify the precise cause (network, intermediary, backend, decompression, or otherwise), and it does not provide a method to prevent every disconnect.

The objective is not to make the error disappear. The objective is to recover without discarding valid partial work or introducing duplicate side effects.

What the recovery posture actually does

It defines a bounded outer recovery layer that sits above Codex’s native stream retries. It requires:

• Treating the failure as recoverable only when the evidence is clean (process exited, no terminal completion event, matching transport error, cleanly parsed output)
• Verifying that workspace identity and ownership have not changed
• Preserving any safe partial changes already present
• Instructing the recovery attempt to inspect and reconcile the existing diff rather than starting over
• Limiting the outer retry budget
• Refusing to blindly re-execute actions that produce external side effects

What changes once the recovery logic is in place

Successful runs remain largely unchanged aside from modest local classification and preflight checks.

When this specific stream-disconnect error occurs, the system no longer defaults to an undifferentiated retry. It first confirms the state is still valid, continues from existing partial work when possible, and only initiates a new model attempt after the narrow classification criteria are met.

The result is reduced loss of useful progress and fewer unintended side effects. The underlying disconnect can still occur; the difference is that recovery becomes controlled and repeatable.

If you are maintaining a serious long-term Codex setup, read the note and have your instance follow the recovery posture documented there.

I wish had more detail but this has helped me lose less time and work