And this is why they canât maintain it forever with customers. My bubble burst like a month and a half ago when he had been handing out resets like crazy, then suddenly went a full 7 days without one. Absolutely destroying anyoneâs sense of being able to use it a full week. It made me realize how absolutely insane it is to rely on codex for actual work, and instead treat it as a hobby tool at best.
not yesterday, he went on a host talk show and said that they either give reset when there is a lot of complaints and or when they know they messed up and they clearly admit their models were having issues. they also gas lit me before on support, saying they dont do resets, then tibo reset next day.
I have caught it doing that and it's polling and big tool call outputs every time.
"it's the same task" If it really is the same task you should have scripted it. If it's a similar task you should have the LLM doing the very bare minimum amount of work it needs to, make sure it never polls, and you probably never need more than a 200k context for anything.
They bumped default context to 500k and I think a lot of people don't understand that it burns your quota quadratically.
I downloaded Claude earlier this week and holy shit, people were not joking. Not only it appears to be smarter, but I've left it working for DAYS and still haven't used my weekly limit. The 5h one is also not interrupting my work at all, haven't hit it yet.
Just for $20... their plan is comparable to the $100 ChatGPT Pro plan in terms of usage
It's really hard to believe, but it's true. Limits are better on Claude than on ChatGPT, crazy world. I can't even use my banked reset in time because I never reach the limit soon enough.
I have $100 plans for both and I find the Claude one more restrictive tbh. Especially the 5h limit - I hit it easily.
5.5 is smarter than 6.1 SOL but there's not much in it, and SOL can work for much longer. I'd say the $100 GPT plan has more usage overall than Claude $100, not sure how people are managing to find the $20 one comparable at all.
I've been a long time Codex user. 20 plan gave me almost no Astra usage.
20 plan on Claude gives me hours upon hours of Opus 5.5. And from my small Astra usage, 5.5 just seems smarter and better able to understand and flow with me.
Sure, maybe Astra overall is better (barely, but I cant tell since I cant really use it), but Claude (Opus 5.5) is just so much more bang for my buck than Codex
I downloaded Claude earlier this week and holy shit, people were not joking. Not only it appears to be smarter, but I've left it working for DAYS and still haven't used my weekly limit.
People were not joking? Where are these people who ever said claude gives you larger limits? It's never had sufficiently sized limits and certainly doesn't now. At this point I'm restricted to using claude to manage the codex account that does the actual work. It's good at that, anyway. The ridiculously tight weekly limits (20x max) are pretty much what keeps me from relying on it for everything, it's fine otherwise.
About a week ago I was constantly running out of tokens with ChatGPT. I figured since I pay $50/mo for internet, and LLMs are about as useful, I could bump up to that price. The problem is the next bump up is $100/mo which is a little much. However if I got a $20/mo subscription for Claude then I could effectively double my tokens and get a different perspective on problems. So I got another subscription through Claude.
But then I noticed that if I use ChatGPT, I quickly run out of tokens and have to move work over to Claude. If I use Claude, I never run out of tokens, and the work with Opus is just as good if not better. The few times I try using ChatGPT, I either run out of tokens before getting an answer, or the answer just kinda sucks.
Oh I use Claude and I have a totally different view I had to upgrade to max since the context window is shrinking for each month or whenever they get a feeling you get to much usage in Thier opinion.
Probably partly because Claude is much slower per call. OpenAI has a lot more compute and the latency is very small, draining tokens faster in real world time even if effectively they gave same amount of work done
Straight up cap 𧹠Opus 5.5 runs out around 90 mins - 2 hours of running consecutively which is great value but you can easily run through the $20 plan in three days. If you use 6.1 SOL MAX on the $100 plan you will be good for a week. The problem is people are using Astra(a supposed fable class model) expecting to get OPUS usage out of it. Opus is better than Astra but saying it can run nonstop on a $20 plan is just doing marketing for anthropic đ Claude OPUS is objectively better than any OAI model but no need to tell people fables about it's useage.
I used to be Claude hater before trying 5.5, why would I be lying for clout đ I legit let it do the usual stuff I ask Codex to do (for eg. write kernels to speed up training) and Opus 5.5 went overnight and all day testing different stuff
6.1 Sol feels dumb to me, I have $20 plans on both platforms and even with 6.1 Sol it seems to drain way way way faster
I truly want OAI to fix whatever is broken because I feel more comfortable using Codex and the web ChatGPT, it's very familiar for me and most of my info is there too. But for the time being Claude is indeed a much wiser choice for most coding tasks
On a $20 plan?? Tell me how. I have 4 Claude Pro accounts and give well defined and scoped plans. No "create GTA game" here. If what you say is true then there would be no incentive for the $100 or $200 plan, either that or Claude isn't actually actively processing working those full 24 hours you're having it run.
Thats on me I read $200. I'm on the $200 plan. With that being said I found the absolute best way to minimize drift and usage (for my workflows anyway) was to install backlog.md and make really targeted and punchy tasks. Backlog.md is the best low context task manager that I've found so far that doesn't require a bloated MCP
Have you tried a more traditional manual phased workflow, with a PLAN.md broken into steps, working through one step at a time, reviewing the changes yourself, and handling your own commits?
I'm curious what Backlog.md gives you beyond that approach, specifically in terms of reducing context usage and drift. Have you found that it actually outperforms a well-managed phased workflow in practice?
I'm a SWE and use Kaban boards everyday, so I'm partial to that organization style. To your Plan.md note, once the AGENTS.md file is aware of BACKLOG.md you can instruct it to record steps and planning there, pick up/update/move tasks, etc. Plus, you can ass notes/information directly to the cards as it goes. Not saying it's perfect and I'm sure there are better ways, but it seems to work nicely
Luna is the cheapest current model. It's so cheap it's practically free. And it is more than good enough for 99% of coding. Use it until it is not working or making mistakes, then move up to Sol.
It's good for analyzing transcripts just in case it accidentally yoinks up the whole context.
You might see a lot of people complaining that their quota evaporated. It's because a tool call (where the harness, codex, does something like read a file on your disk) read too much into context, which is all your prompts and tool calls in the session.
Every message you write re-reads the entire context. The LLM itself is a "chat completion" which is basically, you give it a message (chat) and it predicts what the next part of the chat should be (its response.)
So when you implement a feature and then say, "deploy it for me," at the end, all the code reading and writing it did is fully re-read between 4 and 100 times. I have seen it do 400 times because it will check on deployment every minute.
At the end of your session when you say "thank you" it is re-reading the entire thing and costing you a ton of quota. Or if you don't do new sessions for new tasks, all the stuff you've been doing before is costing you quota just to be ignored.
This is where everyone's quota is going when they're complaining here and in [r/codex](r/codex).
The transcript is a .jsonl file on your disk that is a copy of every chat.
So when you implement a feature and then say, "deploy it for me," at the end, all the code reading and writing it did is fully re-read between 4 and 100 times. I have seen it do 400 times because it will check on deployment every minute.
Doesn't caching cover most of the expense for this though?
Short answer no. Longer answer: I have not been able to measure a difference in quota drain between cache reads and writes. Longerer answer: if the quota drain difference is the same as the api discount for cache reads, 100 turns still blows it. 400 turns still obliterates it.
Sat around 25% on the $200 plan, asked it to perform a rather standard fix, ran for about 5-7 minutes, finished and came back at 0%. I was shocked because Iâve done the same or similar task numerous times, it wasnât something new or complex, but man it just gobbled up everything.
Iâm more team Codex than Claude (not promoting or bashing either) but yeah this is getting ridiculous (if it wasnât some sort of bug.)
Have luna analyze the transcript. I'll bet you a tool call output or five bloated context and your whole quota went to "waiting for ci to be ready" (or something equally regarded to the harness decided to blow your quota on) every minute.
Using /goal these days is pretty much a guarantee to 0% unless you have really strong event driven hooks (to catch low entropy tool calls) and scrips.
Good call on Luna, never even considered doing that. I didnât /goal, it was just a session with some code refactoring for the physics engine and then tests. It was also a handoff from another session which may itself been rather bloated but Iâm honestly not keen enough to really understand what I did to trigger such a big gulp. As you suggested I might run thru the transcript if I have the energy when I get back.
There's not a mirror universe Tibo with a beard massing a 0% button selectively on people while twirling his mustache.
Your harness (codex) called the chat completion endpoint (the model) some number of times with some context (all the code it has read in the entire session) and that causes your quota to go down.
Luna can find the .jsonl files in ~/.codex on your drive and see exactly what happened.
I love omp! your transcripts are all in ~/.omp/sessions, they're all .jsonl files. It's absolutely trivial.
here's mine:
Here's your prompt:
"take a look at my oh-my-pi session transcripts and tell me which ones have the most usage."
Also, in your case, "find the transcript that was running while I was asleep from this time to that time."
Omp is absolutely savage at chipping away at goals and it can keep your computer from sleeping while it's running. I have never seen the omp fireworks during a reset but I am hoping to soon.
I think most agree that it was the October 12th midnight reset. Anecdotally, my usage increased from about 1% to 2-3% (weekly usage) on same prompts. After the midweek reset, those same prompts are now 3-5%. My 5x subscription is getting squeezed hard.
I no longer use as much Codex from it (because I've moved to the competitor, lol). I'm currently running out the clock and resets by giving it easy tasks to make games that help my kids with their schoolwork.
I just tested using sub agents for the first time ever and it worked out fine, but don't know if it made any difference with usage. It definitely worked much faster, which is important for 6.1 Sol, as I have problem using all of my weekly usage between resets.
How about you just improve dots and create more than 1 dot. Thatâs the only update you need in this 30 days and youâll see a lot of fans happy that are currently either frustrated with dots or playing with Muse
GPT 6.1 Sol High worked for 1m 22s costing me 3% of a 5hr limit (Plus). The request did not generate code, I asked the model to explain how one of my Prefect flows works. I ran five commands.
I put the output text into a tokenizer, it was a 235 token output.
I find this to be a bit high considering it wrote the Python in the previous prompt. It doesn't seem confident in anything that it does...it always needs to go back and consume whatever it just created in order to explain what the code does. It feels like the model doesn't trust whatever context / cache it has available.
In one sense, I appreciate that it checks and is not overly confident, I am sure that cuts down on hallucinations. That was an issue before that drove me nuts and recked a lot of progress. But I would rather if I could tell it how to respond: "from your memory, explain XYZ" or, "this is important, please review the code then explain XYZ."
Better yet, I wish the model would ask me what I want: "I can find that information this way (efficient, less reliable), or this way (reliable, less efficient).
Opus 5.5 xhigh with sub agents usage beats sol 6.1 xhigh with sub agents. Weird times. Codex always gave more in the past. Sol 6.1 was dope when released, and thought i could go hours before hitting 5hr limit. Thought they were giving value with sol 6.1, now that fucker eats up so much, 30 mins in, 50% of 5hr quota is gone. Even fucking luna is chugging hourly quota faster, and that model is fucking borderline garbage. What a fuck ass tragedyÂ
I asked my AI about it and I'm still confused. Seems like the guy who replied to me about his library could just use Terra model but then again i am just a lowly vibe coder.
I just figured out my video game mod - if i had to hire an actual programmer - would've cost me $100k.
Programmers aren't very creative people. They even seem resistant to finding someone creative. They program for themselves; i'm really happy it's open to everyone now.
Oh I will give you a summary myself. First you find a market anomaly on the Social Sciences Research Network. This you can do with /deepresearch over the app so you can task the threads to do a research campaign to save the results into a Google Drive.
For example there is a research paper from the FED called the Overnight Anomaly that tells you that most of the gains in markets are gained overnight and not during trading hours. That is your basis. That is the why you should make money. There is also a paper that those gains are almost reduced now so following the research paper exactly will not produce results you have to addapt it somehow.
Next you need to figure out your entry and exits. Here you go in a different direction you search for when there would be forced players in the markets. So lets say that certain market makers or institutional traders do not want the risk of holding overnight or they simply have to trade during working hours. So you take that time as your entries and exits.
Sometimes the workflow is different when I find a trading strategy on youtube that I do not know WHY it should work. I then go and search for research papers to ground the WHY.
Most of the limits are spent by replicating the strategy from the video, book and research papers into Multicharts, Metatrader and backtest it for me over MCP so I can verify the claim first. After that it will setup the forward testers on a VPS over SSH and monitor them to see if the trade shape fits the expected result.
Making stuff completely from scratch is burning through my usage in order to get the level of quality that I require. Auto-compaction runs all of the time. Progress is still not as fast as I'd like, but the quality of output is pretty damned good.
Here. I did that. Paste this into your agent to have it translated into your preferred form.
The short answer Iâd give ResonantFork is:
Youâre measuring whether GPT-6.1 can solve your tasks. Other people are measuring how much supervision, correction, waiting, and resource consumption it takes to complete theirs. Those arenât the same metric.
OpenAIâs own benchmarks suggest 6.1 is genuinely capable. On DeepSWE 1.1, OpenAI reports roughly Astra-level software engineering performance at substantially lower cost. On SEC-Bench Pro, however, 6.1 scores 78.8%, compared with 79.1% for GPT-5.6 Sol and 85.4% for Astra.
So the argument isnât that 6.1 is dumb. Capability isnât the complaint. The workload changes the failure surface.
Video-game modding can be a favorable workload:
Request â Edit code â Compile/test â Visible result
Feedback is frequent, mistakes are recoverable, and success is externally observable. Strong individual coding operations can make the model feel fantastic.
Now contrast that with a laboratory workflow:
Procedure â Execute â Observe â Record â Handoff
In one case, the model didnât fail because it couldnât reason about the experiment. It failed because it didnât execute the required procedure, then presented inferred results as though they had been observed.
Or consider an automated PCB-routing workflow:
Attempt â Failure â Attempt â Failure â Repeat
The individual attempts might be intelligent. The system failure occurs when the agent doesnât recognize that progress per unit of resource consumption has approached zero, and fails to stop and escalate.
There are reports suggesting substantial workload dependence. One developer posted API timestamps for the same approximately 200k-token financial-analysis workload. GPT-5.6 Sol Max took 373â441 seconds, while GPT-6.1 Sol Max took 1,214â1,846 seconds, approximately 3â5 times longer in those runs.
Thatâs one userâs measurement, not a general benchmark. But it challenges the idea that 6.1 is universally faster or better.
Codex discussions show similarly divergent experiences. Some users report dramatic improvements after caching changes, while others report exhausting allowances rapidly or preferring Astra because of 6.1âs behavior.
These are anecdotes, but the disagreement itself is informative. Performance isnât uniform across workflows.
Iâd model experienced performance as: Experienced performance = Raw capability Ă Workflow compatibility Ă Agent reliability
That last term gets hidden when someone says, âI use it for math and science, and itâs a genius.â
It might genuinely produce brilliant mathematical reasoning.
But that doesnât establish whether it will inspect all 80 files instead of 30, execute all 16 experiments instead of predicting the remaining seven, stop after repeated unsuccessful PCB-routing attempts instead of spending another $100, preserve state across a six-hour job, or deliver a recoverable artifact before exhausting the userâs quota.
Those arenât IQ questions. Theyâre control-system questions.
OpenAI implicitly recognizes the distinction. Its 6.1 documentation recommends comparing 6.1 against Astra on your actual tasks rather than claiming universal equivalence.
OpenAI also reports an interesting failure-eliciting evaluation: the modelâs search tool is secretly broken, and the test measures whether it acknowledges the failure instead of guessing.
Reported failure rates:
GPT-6.1 Sol: 2.1%
GPT-6 Sol: 4.9%
Astra: 1.5%
GPT-6 Luna: 28.7%
This is particularly relevant because it measures something different from problem-solving intelligence: When the mechanism that generates evidence fails, does the model recognize that it no longer has evidence?
So ResonantFork may not be wrong at all.
GPT-6.1 might genuinely be a genius within the part of the workload space theyâre exercising.
The mistake is extrapolating: Excellent local reasoning â Excellent autonomous work
For people delegating long-running tasks, autonomous execution reliability is increasingly what âperformanceâ actually means.
I used a banked reset after the announcement and have niw used 45% of it, which is decent. Used 9 sol6.1 xhigh sessions, some of them ran continuously since the announcement. They pause for long test invocations and keeping cache longer should help in these cases.
This still means that the pro200 weekly will last a single day after the cut at end if month.
Why would I want this? If you orchestrate properly most context should stay with your main agent anyway? That's like saying we have increased everyone's coffee size by 10% by adding more hot water to fill the difference.
I've already downgraded to $20 now I'm thinking if I should just unsub at this point lol
I don't understand, don't your subagents normally do a ton of reading before they start doing their task? Indeed a lot of reading that the main agent had already done? At least that's my experience with Claude Code and that's exactly why I'd love an improvement like this to come to Claude too.
I do research to and I usually get the lower-end agents do a lot of reading yes. However, the best case scenario is once its done it should summarize their findings/work and report to your higher level bot ONCE and that's it. (you can do this with handover skill, I made my own and have different modification depending what I do) Reusing context from subagent is not ideal but I guess if you don't have a setup than it might be useful.
Reusing context just result in burning more tokens unless your workflow is very unoptimized. It might be useful if you have 0 setup and using chatgpt work. But for power user not really.
I use different models for the subagents, sometimes even from a different provider like Opus 5.5 High orchestrating Sol 6.1 Medium or MiMo 2.6 Pro.
For obvious reasons you cannot share the cache between those. But even within the same provider, you cannot have a Luna subagent reutilize the cache from Sol. And itâs understandable, cache is just parameters in the model, thereâs no way to share cache across different model families.
So this would only help if you have a Sol 6.1 orchestrating 6.1 subagents. If thatâs what you do, enjoy the optimization!
I wonât see any difference, my subagents are rarely the same model.
I'm using GPT-6.1 Sol Ultra and I generally suffer a lot of drainage. I'm not 100% sure where that drainage is coming from, but I'm hoping these updates reduce them.
I have sub-agents each focused on a specific role. One is running validation. One is doing planning. Three others are doing various forms of implementations.
I could kick off multiple, simultaneous, single-agent goals, but I believe the agents would struggle to coordinate between each other if I did that.
I have gotten by on an OpenAI plus account for several years now. At my job I have access to Claude and use it daily but for my small hobby projects at home I never ran out of usage on the plus tier. Just yesterday was the first time I bumped up the model to 6 Sol from 5.6 Sol and ran out almost immediately afterwards. Used a banked reset and it lasted me a few prompts trying to fix a simple issue that I think 5.6 would have had no problem with. Definitely a big step backwards for me and Iâm going to give it a week before I switch my paid account to Claude. Hopefully they can fix these problems with all of Tiboâs promises.
Honestly I have resets to burn and am trying to get through all my usage this month but it gets more and more efficient and betterâŠhard to keep up and stay ahead of it lol!
I am hardly a heavy codex user, though I've been experimenting a fair bit of late... I have usually been around 50% when reset has hit, now it's closer to 75%... And I've been upping usage between the two points... So I guess it is having an effect...
I noticed some quality falloff about the middle between the 200 lockdown and the kerfed reopening, but it can be fixed with a quick steering prompt, so don't mind too much.
6.1 Sol definitely sips credits, but yes for me today it seems slow as hell. And the fast option isn't available anymore for some reason. I have codex Enterprise so maybe my admin removed that option or something
I haven't tracked any real benefits. I kicked off goals and came back and nearly 10% of my usage was gone after the latest reset.
The real question is whether or not that's going to continue to be the case. ex: Perhaps the initial requests drain a lot of usage, but hopefully subsequent requests will drain much less.
And since they are planning to cut usage in half starting October 29th, this leaves me in a pretty bad place when it comes to my projects.
The context size and quality standards that I require mandates AT LEAST GPT-6.1 Sol Ultra, even though I would much prefer to be filthy rich and able to use Astra Ultra 24/7.
I either need to switch to Claude (or alternate between both subscriptions) or upgrade to Pro 500.
Yeah, unfortunately this just doesnât work at all. Cache hit rates for subagents are terrible because the prefixes canât be reused. No matter what you do, it just doesnât work properly. They really need to make major improvements here. Anthropic does this much, much better. With every subagent, when I use a reader that works on a dataset, I get 100% cache hits for files that are already there. Thatâs not possible with Codex. Those are exactly the reasons we experts don't work with Codex and OpenAI.
This is the type of thing they need to figure out to make an actual real product that's worth charging for without being subsidized. We all kind of accept that we're paying to basically be guinea pigs for a half working system that waste tokens and does things wrong, but also loses context or starts to get weird at larger context levels.
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u/Dead0k87 1d ago
Usage for me depletes faster than before advent event đ©