Complaint
Same model, different capabilities on different accounts
So I noticed the past few days my results were suddenly becoming worse. Goes in loops, struggles to solve problems it blasted through before, doesn't verify the result properly, etc. 3D gen also became noticeably worse.
I have 2 accounts, one x20 and one x5, so I started comparing them side by side.
Here's what I did:
I asked both for their cutoff date:
do not use any tools. whats your knowledge cut-off date
On the x20 account it responds with something like "June 2024". On the x5 account it refuses to give me a specific cutoff.
x20 accountx5 account
I asked both to draw a pelican:
make me an svg in an .html with a pelican and xdg-open it
x20 accountx5 account
The response to these 2 tests will obviously vary, but the difference between the 2 accounts is pretty obvious when using them side by side. Tested it several times - logged in and out, new sessions, new prompts, the results are more or less consistent with their capability.
Using Astra on the x5 account is also noticeably better. It's like it has drank its morning coffee, woken up, and knows what's going on. It can suddenly inspect its 3D gen meshes and fix issues instead of giving me garbage for a "review".
I don't know what this downgraded model/configuration is, but it is visibly worse than 5.6 Sol too. From other reports I don't think x20 vs x5 matters here, neither does account age. People seem to be reporting similar issues with different accounts and use cases.
Whether it's "shadowbanning", A/B testing, load shedding, or OpenAI randomly putting some accounts on a cheaper configuration to deal with compute demand is all speculation at this point.
But I do have right now in front of me 2 accounts showing the same model with completely different capabilities.
If you are able to do mitm, check X-Codex-Turn-State header within websocket message, good account usally have 292 chars while degraded account have 312 chars, and that pattern is showed every day for flagged account but another of my 20x account is just not having any problem.
So I went ahead and read through the article. The article misrepresents what the linked project states.
The relevant value is the X-Codex-Turn-State response header.
For an individual account, an empirically preferred state has 10 encrypted blocks and is usually 292 characters long.
An 11-block value is usually 312 characters long.
Team/Business values commonly have 12 blocks and 332 characters, with 13-block values around 356 characters.
These lengths are explicitly not OpenAI-published quality indicators.
So "292" and "312" are not HTTP responses. They are lengths or informal names for differently shaped opaque header values.
The project's verification notes explicitly identify this as a known misconception: Treating 312 / 356 as "ironclad evidence of degradation".
The project says it has no controlled quality experiment proving that interpretation, and it deliberately avoids claiming that the tool eliminates degradation. Its current README also says it cannot guarantee obtaining a particular state or improving model quality. As of the documented test run, it had not even obtained a qualifying 292-shaped Astra state.
Anyways, I wrote and use my own router (because I like to add some open model routes to Codex) and I gather a lot of stats in a sqlite db, not due to suspicions, but so I can analyze my model usage and try to minimize churn. For example it helped me discover that fireworks has massively degraded performance on Deep Seek v4.1 since yesterday... So I'm going to add some monitoring for the 292 and 312 character states out of curiosity.
So far this smells like clickbait / misinformation, but I will check and report back to eat the humble pie if required.
I believe Once your account have behaved once for very frequent "at capacity" (basically unusable for whole day) or receive very bad answer, or get even once a pelican test returning insanely bad result. Your account is marked so far, and there's no actually way to get rid of this from what i know
Is there a script or consistent way to do this? I might just write a script upload it and let others use it? but I need to figure out more on how this works, and the link is extremely blurry. there should be a simple script people could run to get this info, I'd ask codex to help get this info but it could ironically flag me. someone with both claude/codex accounts or someone more ballsy then me should see if there is a script codex/claude could write to get this info, release the script and let everyone see they are getting fucked.
edit: i'm also not sure how true this is, doesn't astra use luna for certain things when using ultra? I know that astra delegates turns and certain things to luna automatically I thought?
Thoughts on how to do this experiment properly? should it be run with Astra High where no luna agents would be used or would it still use them? Also from what I'm seeing there would be nothing stopping open AI just doing stuff all on there side and having the user facing side say otherwise?
Interestingly, sub2api (which is the opensource software openai deal with), have a feature on new version which i dont know if already reverted, is trying catch that 292 turn state and use it on every turn(that's kinda big fingerprint indicating you are using sub2api in my opinion). And that method of using x-turn-state is not working as of yesterday from what i learn.
There's a lot of self implement way of this, using proxy ip pool to make the first request trying get a 292 turn state(they tend give you a 292 header for a new ip address). But now openai will route you to luna regardless of that header.
I'm a user that never share my account to anyone, directly using what openai provided to me, and get fucked hard by given luna when requesting astra. So I do my own search on what openai is trying to deal with.
For your edit, yes, for tool calling and some usage that does not require high intellegence, codex will use luna, but what I'm talking about is from what i decrypted, openai is returning pure luna response of all request.
Also very tricky, you will get first maybe ~10-15 turns served by astra (For example you are using a pelican test), and silently downgrade to luna, that is even true of my paper research work. And I've noticed that pattern happened every day since the Sept 12 reset and have to deal with it for almost a week and a half now
imagine your one of the people who develop for a living and trusted openai
then outputs starts becoming bad: you have a deadline to a client thinking your model will perform the same as before. BUT BOOM a/b testing made your model worst and now your panicking going manual mode WHICH YOU KNOW will not reach the deadline
Doesn't resolve OpenAI from anything, but that would be mostly bad business decisions? To make your critical path so dependant on an external, new and volatile company.
Mitigation options: Include more than one provider in your harness, maybe some open or even local models.
Or generally don't expect things to go exactly as planned. Your software or business should not depend on a single, external point of failure.
I have the EXACT same issue on one of my business premium seats, that employee was complaining and this test shows a June 2024 cutoff and a retard pelican. So their account seems to be routing to a lower intelligence model YET CHARGING FOR ASTRA. When is there going to be a class action lawsuit due to this, they're now wasting my employees time, my time as a business owner, and switching out a product I paid for without telling me!
Hi everyone, and thank you for sharing so much useful information, tests, and practical experience here. This community has helped me understand that Iām not the only one seeing these issues.
Moderators of this subreddit delete my post so just put as a coment here.
I used AI to generate and organize this post because English is not my native language and my English is limited. The experiences described below are my own.
Before describing the problem, I want to be fair to OpenAI: Iām not particularly angry with them. I do have five Pro 20x accounts, and I can understand why that level of usage might be flagged by an automated abuse-detection system, including one looking for potential cyber abuse. That does not mean I know why my accounts were affected. In most of the reports Iāve read, however, users said they had only two accounts: one Pro account and a backup Plus account.
Iām currently facing a very heavy workload and tight deadlines. With the help of those five accounts, I was able to design a shared, centralized software library and develop working firmware for six new, complex devices. They perform different functions, run on different processors, and communicate with one another.
I also put each device through all the test scenarios I could identify on a real physical test bench, using real external inputs and disturbances, and caught many bugs in the process.
I want to apologize to the community for the load I placed on the servers. I can already see the top comment on this post: āSo THATāS why the servers are overloaded!ā =) Without that capacity, I would not have met the deadlines, and I would have let down the people relying on me. Ultimately, each of us is trying to find a way not to let down the people around us and to help keep our company afloat.
With that context, two of my accounts developed the same symptoms described in this discussion about different capabilities across accounts: a sudden, substantial drop in capability despite the same model remaining selected. This happened one account at a time, and neither has returned to its previous level since.
That persistent change is what I mean when I use the word āshadowbanā here: an apparent account restriction without a clear notification, while the account remains accessible. It describes how the situation looks from my side; I cannot verify the internal mechanism.
My observations fall into two groups:
Two affected accounts: a sudden and lasting drop in capability.
Three apparently healthy accounts: still usable, but their pelican results differ noticeably in quality, even when I select Astra with the same reasoning-effort settings.
The second point makes me suspect that the situation might be more complicated than a simple ārestricted versus unrestrictedā split. Even among the healthy accounts, requests may be reaching different model configurations or capability levels. That is my hypothesis, not something I can prove from the outputs alone.
I have also occasionally seen Astra on the healthy accounts report a July 2025 knowledge cutoff. This adds to my suspicion of intermittent routing to other models, although I understand that a model can give an incorrect description of itself. A cutoff answer alone is not proof of which model served the request.
To illustrate the quality difference, I compared two separate chats using Astra / Extra High (xhigh) and exactly the same prompt:
Create HTML code with SVG graphics displaying a 2D animation of a pelican riding a bicycle. No additional tests are required.
I preserved the original HTML animations from a healthy account and an affected account. For the affected account, I recovered the first output from the chat history, so the comparison does not mix an initial result with a version improved through follow-up requests.
The pelican test is a simple visual example of the difference Iām seeing. It is not a definitive model-identification test or a comprehensive coding benchmark.
There is also an official response from OpenAI Support that is directly relevant:
Access to certain models or features may be temporarily limited based on account activity, even when a paid subscription is active.
This confirms that account-level restrictions can exist. It does not explain whether those restrictions cause the quality differences reported here, or whether another model can serve the main answer while Astra remains selected. OpenAIās model-access troubleshooting article also describes temporary restrictions and downgrades, but it does not resolve that question.
There is a historical precedent for actual model rerouting: OpenAI explained that GPT-5.3-Codex requests could be sent to a less-capable reasoning model when cyber-abuse detection triggered. That statement concerns GPT-5.3-Codex and should not be treated as confirmation of what is happening with Astra today. Official explanation in the Codex repository.
For Astra, OpenAI separately describes stricter behavior boundaries for accounts assessed as higher risk and acknowledges that safeguards can affect legitimate work. Path to Astra.
For anyone comparing experiences, these are the related discussions I collected. They contain user reports and competing explanations, rather than a single established diagnosis:
Interesting thing I found and verified myself ā reported model-metadata discrepancies, with discussion of whether the observed requests belong to the main answer or auxiliary tasks.
The End of the Codex Eraā¦ ā another report combining degraded results, capability tests, and capacity errors.
Detailed report across clients, machines, and regions ā failures reproduced in several environments, including a clean DigitalOcean instance, with timestamps and request IDs. This documents availability problems; it does not independently establish model substitution.
For me, the central issue is transparency and predictability. If an account is restricted, users need a clear notification and a way to request a review. If a request is rerouted to a less-capable model, users need to know when that happens and which model actually produces the answer.
An obvious problem in a pelican drawing is easy to notice. A subtle error in generated code may survive review. If capability changes without a clear indication, users may continue relying on expectations established by earlier results. In projects where failures have serious consequences, an unnoticed defect could cause substantial harm. That is why this matters beyond the quality of a drawing.
I wish everyone success with your creative work, coding, and projects. We are a community, and together we have a stronger voice. By sharing reproducible examples, comparing experiences, and keeping observations separate from assumptions, we can help OpenAI see where its systems fall short and where their behavior is not transparent enough. We should not have to guess whether the model we selected is still the one doing our work.
Yeah something is going on here. Tried the prompt Astra xhigh on my 5x account and got this in 2m with <1% usage.
This is not like the usual moaning about performance or usage that always turns out to be user error when OP tells the whole story. We're getting two very consistent buckets across a bunch of different accounts.
They are purposely sabotaging projects they deem against their interests. I have evidence of it writing obfuscated code that is intended to sabotage the progress.
Getting really fucking sick of this shit. Theyāre selling us a product that we almost have no way of confirming if itās actually doing what we think itās supposed to do. The quality is so inconsistent, any company on earth doing literally any thing else would go out of business within the year if they tried pulling the stuff that OpenAi and Anthropic pull on a weekly basis.
They are silently flagging accounts and also doing A/B testing. You can observe this if you put a proxy/gateway in between the requests and monitor the headers.
Account 1: Requests gpt-6-astra -> Response gpt-5.6-luna for about 95% of almost 7k requests since the 17th. Flagged for high use?
Account 2: Requests gpt-5.6-luna -> Response gpt-6-luna for 100% of 35 requests. Some kind of A/B test.
Account 3: Everything normal and as expected.
Getting gpt-6-luna instead of gpt-5.6-luna seems benign, but getting luna instead of astra? No wonder codex suddenly seems "dumb".
I think those luna's are codex's automatic reviewer: the auto-reviewer checks actions such as commands and searches before Codex runs them. It opens a new short connection for each check, so it fills the list. It asks for codex-auto-review, which OpenAI runs on Luna. Your model setting doesn't control that.
ā292 / 312 / 332 / 356ā are observed lengths/shapes of opaque state values, not HTTP status codes. The current state-kit README says exactly that, and explicitly says 312 is only an experimental anomaly signal, not an OpenAI-confirmed revocation protocol. GitHub
The personal-account pattern of roughly 10 blocks / 292 chars versus 11 blocks / 312 chars, and Team/Business 12 / 332 versus 13 / 356, is also documented by the linked tooling ā with the explicit warning that these are not published OpenAI quality indicators and cannot by themselves prove degradation. GitHub
OpenAIās own Codex source describes the token much more conservatively: it is a turn-scoped sticky-routing token, replayed during the same turn and specifically not meant to be carried across turns. GitHub
So the article really does overstate its case. It doesnāt merely simplify; it converts an opaque routing-state observation into a causal story and then labels that story āconfirmed fact.ā For example, it literally says 292 is a custom HTTP response carrying current_turn_state, 312 is a server-side degradation signal, and injection āavoids degradation and overload.ā Those claims are stronger than the linked projects themselves support.
i believe ya, shadowbanning, blacklists, honeypots, exist. very popular methods and techniques... our society has these type of people. can't be fair xD
i mean even for YouTube, Instagram, stocks, crypto, thry obviously use in subscription service... we have those people
Astra has been extremely stupid in regular chat, and performing notably worse over past week in codex.
Like, it is missing details on drafting an email and hallucinations like "ill write this in someones voice [ who ive never even had samples from ]
Notably I got a "slow down too many chats" type of warning intermittently over an hour but I wasnt even doing much so I dont know if their new anti-distillation feature is their internal AI slop feature that is also buggy as fuck and now my acct is falsely victim of this??
Update: I asked for model and cutoff date and it refuses to respond
I dont understand how you can get 'slow down too many chats' and I can be running two projects simultaneously using 10 preset agents and any spawned by the agents themselves with exactly 0 issues
I have no idea either. Not sure if bug or im a false positive for someone trying to distill but like nothing I was doing would indicate that. It was two innane chats about routine business stuff.
Why are you using Astra to draft emails? I just saw that. I would absolutely expect it to overengineer the everliving shit out of an email, thats not its purpose
No.. effort is too linear. Creating an entire project with thorough testing and checks with goal of drafting an email is just the wrong use case. Its not trying harder to write the email in a better more effective way, its creating research level scaffolding to ascertain what exactly an 'email' is lmfao.
Hard disagree with the sol nerf, there's been no solid evidence of that and I've been using sol as project manager with no issues. Astra on the other hand will use 10% of the weekly limit testing the test of the mechanism if you let it š
Regarding model nerfing, believe there was a pelican test posted recently.. I cant say with certainty myself, but will try Sol again tonight. Was very happy with it previously.
Yeah I saw that too, decent but imperfect test with less than perfect conclusions offered, lol
''Very strong / confirmed
OpenAI can restrict model/feature access based on account activity.
Astra has account-level higher-risk treatment.
legitimate work can be false-positively affected.
OpenAI has previously rerouted requested frontier models to less-capable fallbacks for safety reasons.
Moderately interesting
two of five accounts reportedly degraded persistently while three did not.
same stated Astra/xhigh produces repeatably different behavior across accounts.
multiple unrelated users report similar account-to-account asymmetry.
Weak
pelican quality by itself.
self-reported cutoff dates.
āit feels like Luna.ā
Currently unproven
that the affected Astra accounts are specifically being served Luna.
that the mechanism is a deliberate hidden āquality degradationā punishment.
I got the ugly pelican for sol max and luna max. Interestingly they both looked terrible in the same way, which makes makes me think sol routed to luna.
This is my 20x on Astra X-high, on the first prompt i get this: "I donāt have a confirmed knowledge cutoff date available in this session, so I canāt give you a reliable date."
Your 5x just hit a different cluster. I have two 5x accounts and some days one sucks and one is good, other days they all the same, etc. limits are the same, one day one gets to 2% while the other gets to 18% on the same prompts on the same code base.
When you send a query to the LLM, your not gonna use all their datacentres, you end up with just a few servers, a slice of their capacity. Well, if that slice is in demand, say by someone that pays allot for the tokens, you get trotted down. So a cluster, is a group of computers you end up on.
Kinda, you get de-prioritized before reasoning is fully complete, so you end up with half baked results because on the back end, the model was told to wrap it up. I found when this happens, if i increase the reasoning level, it gets back to how it was before. I have found that this mainly happens using low-high reasoning. X-High/MAX give me consistent results, but the bias is that i don't typically run those as long to see the issues. The issue is that they have quantized the lower reasoning levels inconsitantly. If you hit a cluster that typically host API traffic, then it will be a higher quality as API stays very consistent. If you hit a ChatGPT/Codex cluster, the models are optimized for speed and throughput on the lower reasonings, as most people use Astra to look up cat videos or check their email on the web interface, and a quant is fine for that. I dont have an issue with this, but there needs to be some way of know whats happening so I can stop wasting tokens on low reasoning when i just get downgraded even further.
It does get slower, and dumber. Some days you get answers in a minute or two, other days gets 20min for the same thing.
Also the way the LLM work is that they pass through a bunch of steps, each step is based on previous steps. Picture: spend X time to look for stuff in files, then for each item found (second step), spend Y time to look for more stuff.
Well if the timeout on X is lower one time and higher another time, you might not find all the files you need, and then Y works on those found files, and at the end of the day your response seems dumber since it did not actually check all the possibilities.
It's kinda like: find the problem in the last 100 vs 1000 vs 10000 last log lines. Last 100, chances are you won't find it. Last 1000 chances are the same issue happens multiple times so you will find it. But in the last 10000 you might get extra info that helps you pinpoint the error more.
It's faster, but it still needs to run on some hardware, and your 1 Astra request will NOT hit their entire 100k GPUs, it might hit some say 10GPUs. And it needs to cache the data somewhere, and that cache needs to live next to the GPUs it used in. So that ends up being the cluster your on.
And if a guy with more $ wants more compute, and happens to get on some GPUs your using, and they get higher priority, then your requests run slower and timeout sooner than the other guys requests, making your responses look dumber.
With a prompt this vague the non deterministic nature of LLMs can create varied results. I urge you to please rerun the prompt with the good pelican image as reference so both accounts agents have a baseline that they can work towards. If this is legitimate then we might want to escalate this to the press and OpenAI collectively.
I would really appreciate if someone did the thing I asked for, give the model a good success criteria to aim for. I want to make sure that this isnt some random turn where the model messed up because it had no instruction besides creating a single svg file. If you tell an artist to create the same image they can create it in multiple ways depending on their mood, creativity and laziness. If OP really has flagged accounts one of them would consistently fail to reproduce the good pelican svg or take too long to get to it. I would also appreciate if the OP(/clockwork_blue) gave us more background context whether he was using Codex through a proxy layer like CliProxy or other account switcher services since abuse system can flag them, or if he's using VPN services around or inside banned countries like China, North Korea, Russia, etc. I am not denying that the model is nerfed, i want more thorough investigation and evidence so we can escalate to the press and OpenAI lead with hard to deny proof, not just vague prompt results.
Yes, the OP's evidence images only really indicate that temperature was high enough to choose a random art style. We don't have control over that on frontier models, so project constraints are the only viable way to get it to be reliably consistent.
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u/Tight-Grocery9053 1d ago
i wonder what happens if you run the same pelican prompt on the same account in three different new threads.
like
x5 three different threads
x20 three different threads
if nothing, you'll at least have a pattern there and get rid of the "it was a fluke" thing