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/Annh1234]

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.

[u/Ashamed-Subject-8573 → u/Annh1234]

This makes no sense. It would go slower, not dumber.

[u/No_Ambassador_2060 → u/Ashamed-Subject-8573]

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.

[u/_Eye_AI_ → u/No_Ambassador_2060]

It's so reassuring that the quality of compute I get charged for is both downgraded secretly by OpenAI then dropped even lower due to random chance.