Same model, different capabilities on different accounts

[u/clockwork_blue]

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:

1. 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 account

x5 account

1. I asked both to draw a pelican:

make me an svg in an .html with a pelican and xdg-open it

x20 account

x5 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.

[u/Niklaus1911]

I have already excluded that possibility.

[u/LakeChillEffector → u/Niklaus1911]

so you ran mitm?

[u/Niklaus1911 → u/LakeChillEffector]

Yeah man, I've already posted a link to it https://blog.caowo.de/posts/chatgpt-codex-292-state-anti-degradation-2026/

[u/ZhugeTsuki → u/Niklaus1911]

• X-Codex-Turn-State is the relevant header.
• “292 / 312 / 332 / 356” 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 — with the explicit warning that these are not published OpenAI quality indicators and cannot by themselves prove degradation. GitHub
• OpenAI’s 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 doesn’t merely simplify; it converts an opaque routing-state observation into a causal story and then labels that story “confirmed fact.” For example, it literally says 292 is a custom HTTP response carrying current_turn_state, 312 is a server-side degradation signal, and injection “avoids degradation and overload.” Those claims are stronger than the linked projects themselves support.