You still have to worry about misconfigured local models. Even the professionals get it wrong, which is why local model performance is uneven across providers.

I wonder if testing during different time/days show patterns? For example, whether the short circuiting happens more often during workday peak hours.

I've switched 3 months ago to Codex because Claude got incredibly stupid. 6 months ago vice versa. It doesn't matter if you use Codex or Claude. Both will fuck with you at some point. Though Codex probably less.

I have noticed this degradation of 5.5 reliability to what, in my experience, I consider Claude-level of reliability since early June.

My journey dealing with this has been transitioning from 5.5 high to 5.5 xhigh to 5.4 high.

5.4 high has been perfectly reliable for me for the last 3 weeks, and I am happy there.

Occasionally, I run some tasks on 5.5 xhigh to check if it has gone back to being 100% perfectly reliable, but, at this point, I am assuming they are just counting on releasing 5.6 rather than dealing with this reliability issue.

i don't ever believe these issues are technical. They're business decisions to downgrade performance because to fix it means $$$$ and you arn't paying them enough.

Re per token, I had the same reaction, but given both labs are economically advantaged moving customers to per-token consumption... almost want to avoid this on principle. Even if not intentional, benefitting from a degraded product is not something I want to accept or enable.

More now than ever (since original ChatGPT release), the OSS models and open harnesses (eg Pi) are looking mighty attractive.

Right? I also quit Claude Code and switch to Codex over that. Now I’m trying to figure out how I could make an extra $65,000 to never have to be concerned about this nonsense again. I know the economics of using open router etc…

But I’m reminded of ~2008 and the rise of “the cloud” as a marketing term that seemed to me to be a cover for dropping an expectation of rich clients, increasing a companies margins around subscriptions that would chip away at local ownership.

Then I got offput by the zealotry and absolutism around “true FoSS”, told myself I was young and moved on.

And really, a lot of subscription models I kind of can appreciate/ tolerate. Might be irksome but whatever, I get that software is expensive to make and it’s not fair in 2026 to value a yearly upgrade of Photoshop at $200. The capricious UI changes to things that’ve worked for 20 years and they take away say the classic color swatches altogether - silly and dumb.

I can use another professionally necessary tool I pay $200/ mo for, Codex, to whip up a classic swatch plugin.

Is that $200 a fair price for my token usage? I think an extremely heavy month I might’ve used a billion tokens?

But that right there is the problem. They have no idea what, specifically, profitability looks like and are going to be pulling endless levers for … I genuinely have no idea how long - at least through 2030/2032 if we tea leaves their debt obligations?

I don’t want to think about any of that. At all. I don’t want to spend time evaluating model preference and degradation and updating the nuances of how I “speak” to an AI because there’s some mystery backend experiment running on the output I use to produce functional outputs — ie the actual products I get paid to build/ maintain.

AI’s something between a tool and coworking companion, and the capricious “personality” changes due to playing with poorly understood and knobs and levers at the inference level - is maddening. To that end, I want a box in the corner I can point to and know exactly the quality of outputs that no one but myself modifies.

Fireworks?

The vibe-assumed claude code performance regression, yep. People should stop expecting consistent performance from non-deterministic systems. There is zero empirical corroboration of performance degredation.

There has been a step change... in the amount of whining and complaining coders exhibit lately.

But this is model behavior and just a public issue tracker which claude code has just without code? I don’t see how it’s any different than https://github.com/anthropics/claude-code for these issues.

I do appreciate that codex is open source generally, but I don’t think it matters for this class of issue as the model is closed still

I feel openai in general is much more open and real business like compared to anthropic. They’re just a black box.

It’s not just you this is also my opinion, 5.3-codex was a fantastic model in terms of balancing output quality and cost.

Cheap and efficient enough I could afford to use it on basically everything unlike 5.5 or Opus, but still pretty good, I preferred it to sonnet

They rendered 5.3 unusable for me a few weeks back. It simply was locking up or answering poorly.

My understanding of the rumor is that it wasn't OpenAI itself, but one of the post-blip OpenAI breakaway groups (rumoured to be Thinking Machines) who have made a breakthrough and seem to be shopping it to OpenAI. I don't think it has actually been implemented by OpenAI yet.

That was an article in The Information but it didn't read very well to me, I didn't get the impression the author was enough of a technical expert on how LLMs work to credibly evaluate the claim, which came from an insider rumor: https://www.theinformation.com/newsletters/ai-agenda/openai-...

> OpenAI engineers earlier this month told some colleagues they had figured out a way to more than halve the cost of inference, or running existing models, thanks to some newly-discovered optimizations, according to a person with knowledge of those discussions.

It seems to be an inference engine or agent harness defect/misconfig rather. Not only do the issue details not evidence a willful stealth nerf, they actively suggest otherwise: the root cause is crude, and evidently not particularly stealthy (as it's being reported on by a regular user with independently verifiable, exact details).

I don't find "usual user psychosis" particularly fair or tasteful anyhow. You're not left with much more than subjective judgement and speculation/suspicion when all you have is a magic sink of an API endpoint that ingests your context window then spits back a continuation of it. Even if you have a standardized model test suite, claiming a stealth nerf remains an exercise in mind reading (of the people working there). Model quality can degrade without an explicit intention that way, or a downgrade of the underlying infrastructure, after all.

Being tongue-in-cheek conspiratorial, or even actually entertaining the possibility of a nerf, is no psychosis anyways. Not a fan of this trend of people abusing psychology diagnosis terminology like this. I'm sure there are people who go a step beyond and are overconfident in these judgements, maybe in their case it holds. But then that's a minority, and so what you have then is a hyperboly. Doesn't serve anyone.

Isn't the standard to use continuous batching? If they are using continuous batching -- I'm curious why generated token length matters, and why they might be clustering them. If not -- I'm curious why they aren't and what is the tradeoff here.

My first thought would be an adjustment to a reasoning budget parameter (using llama.cpp as my reference) which would lead to these results. But no way to know precisely without an OpenAI statement.

It could be a very dishonest way of scaling to demand during peak hours. I know that some people already scoff in this topic about the subjective nature of perceived performance of models. But the model seemed less smart when US comes online (at least from my testing over the month of May).

On my company blog post from a few weeks ago I felt the need to point this out because it had a perceptively more consistent pattern during those overlap times. Should have saved the session logs for further analysis https://webesque.agency/blog/2026-06-19-llms.html

> In the pass two month barely can do any good scientific writing lately, which of course rely on reasoning. It just writing for gods sake

Honestly, I think this is a really cool sentence. Imagine going back to 2021 and telling someone this was a legitimate complaint about a pretty cheap and very prevalent technology in 2026.

This is an intermittent issue, you should still be able to get your work done. 5.5 was released two months ago so perhaps you're using 5.5 wrong and some things that worked in 5.4 require tweaking your prompts?

You want a refund because of a problem you weren't even aware of until now? And you don't even really know if your work has been impacted by this problem.

Verified this locally myself. Thanks for the concrete test. I guess it's time to give Claude another try.

From some of the numbers I'm seeing in the GitHub issue, the codex desktop app has the same 516 spikes. So most likely it is affected.

Care to explain what you mean by that?

I know that these types of comments are not really popular here, but this struck a chord with me because I feel the same. They aren't remotely close.

I have codex right now purely because they gave me a month free of ChatGPT Pro, so I have been using it in between my usage resets with claude. Since it's "free money" for me I have been using it exclusively on xHigh.

One of my most frequent prompts is "hey codex worked on ____, but it didn't quite hit the mark, can we review the work..."

Yes, part of this is normal even within the same model -- you have the highest power model review the work for correctness, refactoring opportunities, and so on, but man I tell you, I don't know what it is about codex, this is obviously one guy's anecdote -- same prompting style, same repository documentation ala MD files, same skills, way different results.

All that to say, maybe the bug report is on to something here, and it can be fixed.

What?