Discussion summary
Users discuss potential performance issues with GPT-5.5 Codex, especially in reasoning tasks, leading some to consider refunds. The problem may also affect earlier versions like 5.4.
What the discussion says
- Some users report degraded reasoning performance with GPT-5.5 Codex.
- Concerns about nondeterminism and overall reliability.
- At least one user considers requesting a refund.
“I almost never use it for reasoning anymore.”
“Now I want a refund after upgrading to Pro.”
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- Hacker News
- Clearly they are batching reasoning inference in a few multiples of 512 tokens as a throughput optimizationby kleton
- 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.by kbdiaz
- 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
by mhitza - Indeed, it looks like my work has suffered from the clustering issue as well:
Created a script for this: https://github.com/thehappybug/codex-reasoning-token-checkreasoning_output_tokens count percent ━━━━━━━━━━━━━━━━━━━━━━━━━ ━━━━━━━ ━━━━━━━━━ 0 873 28.5948 ───────────────────────── ─────── ───────── 8 64 2.0963 ───────────────────────── ─────── ───────── 9 60 1.9653 ───────────────────────── ─────── ───────── 11 54 1.7688 ───────────────────────── ─────── ───────── 516 48 1.5722 ───────────────────────── ─────── ───────── 12 45 1.4740 ───────────────────────── ─────── ───────── 10 43 1.4085 ───────────────────────── ─────── ───────── 17 40 1.3102 ───────────────────────── ─────── ───────── 13 38 1.2447 ───────────────────────── ─────── ───────── 14 36 1.1792by m3h - When I reviewed the conversations affected by this issue, they did not always align with my feeling of "degraded output".
Some were definitely below par, and I recall having to iterate on the generated code more than I wanted to. However, it is only true for a very small number of conversations.
So we're looking at a small set of affected conversations, and even within that small set, only a few will have degraded output, likely because the model can compensate for the reasoning defect over the long conversation.
by m3h - Maybe its just bad memory but I feel like 5.3 was the best version in terms of token usage and code quality. 5.5 works better but it just eviscerates tokens.by ghosty141
- They rendered 5.3 unusable for me a few weeks back. It simply was locking up or answering poorly.by keyle
- 5.3 was incredibly better than 5.4/5.5. I stuck with it for months after 5.4 was released, and kept testing 5.4/5.5 every now and then but they both were too inconsistent, too rash. I switched to 5.5 a few weeks ago and now regret it, but I am no longer seeing 5.3 as an option to use, only 5.3-Spark, which is trash compared to 5.5.by notfried
- 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
by ifwinterco - A rare case "they made the model dumber" where they actually made the model dumber, instead of the usual user psychosis?by ACCount37
- 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.
by perching_aix - This is the second one in a row now (previous was Anthropic flic in Feb/March)by lostmsu
- I swear some days ago someone here claimed Openai succeeded cutting down their compute cost by half with a breakthrough optimization. So this is it?by siva7
- 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.by SyneRyder
- 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.
by simonw - this explains so much why gpt 5.5 has been so bad lately it was really puzzling why it struggled so much where when it first came out it was one shotting stuff totally amazing, i tried the prompt that will tell you if your plan is degraded:
1. 516, 24codex exec --json --skip-git-repo-check --ephemeral -s read-only --disable memories -m gpt-5.5 -c model_reasoning_effort=high "Do not use external tools. A black bag contains candies with counts: round apple 7, round peach 9, round watermelon 8; star apple 7, star peach 6, star watermelon 4. Shape is distinguishable by touch before drawing; flavor is not. What is the minimum number of candies to draw to guarantee having apple and peach candies of different shapes, i.e. round apple + star peach or round peach + star apple? Give reasoning and final number. The local project dir is irrelevant for this task, do not consult it. "2. 516, 27
3. 516, 12
4. 516, 21
5. 516, 21
This means that the whole time we've been paying for a product that was silently routing to something completely different and inferior from gpt 5.5
Also I read through the github issues and it seems like they closed a previous issue without addressing it ???!!
whooo boy somebody from OpenAI is getting fired over this if not a class action lawsuit is almost guaranteed at this point.
by zuzululu - Verified this locally myself. Thanks for the concrete test. I guess it's time to give Claude another try.by cageface
- The correct answer is 29, right? You could draw all the watermelons and all the round pieces before drawing a star piece. So the model never gets it right, but it does when listing the cases exhaustively?by inigyou
- I love that Codex is open source and issues like these can surface/be addressed publicly.
- I feel openai in general is much more open and real business like compared to anthropic. They’re just a black box.
- 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
by rockwotj - For me, the encrypted reasoning contents, when looking at the base64 string lengtht, show this effect. However, the server-reported reasoning tokens don't. So I assumed it was part of the encryption and/or obfuscation purely. So I don't think there is a real issue.
This is the biggest downside of GPT; thinking is encrypted, so it's more of a black box than kimi/glm/deepseek. You still get thinking summaries though. It's awkward, but workable.
by edg5000 - Deja Vu... This looks just like the Claude Code performance regression back in April. I just quit my Claude subscription when that happened and went to Codex.
Now I'm kinda thinking of trying per token for both, using GLM 5.2 on Fireworks for most tasks, shelling out to the big boys only when needed. Not totally confident I'll break even though.
by resonious - 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.
by jatora - Fireworks?by thatxliner
- 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.
by cududa - 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.
by andrewcamel - You can use this small Python script to display an histogram of `reasoning_output_tokens` in your past Codex sessions. I do see a spike at 516 indeed.
import os, glob, re import matplotlib.pyplot as plt vals = [] for f in glob.glob(os.path.expanduser(r"~\.codex") + r"\**\*", recursive=True): if os.path.isfile(f): try: s = open(f, "r", encoding="utf-8", errors="ignore").read() vals += [int(x) for x in re.findall(r'"reasoning_output_tokens"\s*:\s*(\d+)', s)] except Exception: pass plt.hist(vals, bins=200, range=(0, 5000), weights=[100 / len(vals)] * len(vals)) plt.xlabel("reasoning_output_tokens") plt.ylabel("%") plt.show()by josephernest - I’ve definitely experienced step jumps down in quality on an almost daily basis. I usually used xhigh. The experience of relying on codex’s outstandingly thorough coding earlier in the year has evaporated for me. I’m seeing incredibly stupid implementations intermittently, and have simply switched to Claude until openai takes the issue seriously. As far as i could tell they haven’t taken it seriously for the several months I’ve been personally seeing it.by zenapollo
- 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.by cyanydeez