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GPT-5.5 Codex reasoning-token clustering may be leading to degraded performance
- maille 3mo agotldr: GPT-5.5 Codex model exhibits a clustering phenomenon in which reasoning_output_tokens cluster at fixed values spaced 518 apart. These stuck responses at fixed thresholds are strongly correlated with errors in complex tasks. Observed phenomenon is specific to GPT-5.5; it is much less prevalent in GPT-5.4 and almost absent in GPT-5.2 and 5.3
- joe_mamba 3mo ago[dead]
- ProofHouse 3mo agoPersonally, I would say very likely, to be honest. I gotta go through this a little more, but I actually use 5.5 codex an obscene amount, and I almost never use it for reasoning anymore. It's not even in the same galaxy as far as actually taking out the thinking and using GPT-5.5 or even Claude and then coming back and giving it the reasoning. Blah blah blah, it's the same model. Well, let me tell you, no, it's not, for several reasons, and the delta on intelligence is pretty staggering.
- m101 3mo agoWhat?
- benjiro29 3mo agoCare to explain what you mean by that?
- criley2 3mo agoI'm struggling as well to understand, and I think perhaps they mean they use ChatGPT website with GPT-5.5+reasoning for problem solving, and paste the output into Codex CLI/App. I think they're saying that letting Codex CLI/App problem solve with GPT-5.5 isn't as effective. Essentially that the web harness is superior to the agentic engineering harness for problem solving? Not sure if I agree, but I do happen to use a fair bit of web harness as well, just because I find it to be much more effective at web search and a different type of reasoning. So I must agree a little or else I wouldn't do that.
- jatora 3mo agoI assume they are lying and still think you can use gpt 5.5 non-codex within codex cli. And they outed themselves. A lot of nonsense. And the very poor communication skills just seem like the typical chinese astroturfing you see pretty often now when discussing OAI/Claude.
- criley2 3mo agoSee, this is part of the confusion. There is no such thing as "GPT-5.5-codex". The last codex-branded model was "GPT-5.3-codex". Starting with "GPT-5.4" the main model handles agentic engineering and they did not release a coding model. Both the web harness and codex app/cli use "GPT-5.5".
- dimitrios1 3mo agoI 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.
- kleton 3mo agoClearly they are batching reasoning inference in a few multiples of 512 tokens as a throughput optimization
- kbdiaz 3mo agoIsn'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.
- ACCount37 3mo agoThis "~512 batching" makes me think of things like diffusion or prefill. If they managed to put together some dirty hack that lets them generate about 512 tokens worth of reasoning in parallel instead of in sequence? That would explain it.
- mhitza 3mo agoMy 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 https://webesque.agency/blog/2026-06-19-llms.html
- zenapollo 3mo agoI’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.
- siva7 3mo agoI'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.
- selectodude 3mo agoAt least OpenAI lets me use my own harness. Having to rely on insane PMs letting Claude Mythos go wild on the codebase has not been going well lately.
- tunesmith 3mo agoHow do you mean OpenAI lets you use your own harness? I'm under the impression that a custom harness requires the OpenAI SDK, which requires api tokens rather than plus/pro accounts.
- HumanOstrich 3mo agoAnthropic is the one that prohibits harnesses other than Claude Code on subscription plans and bans users for disobeying. OpenAI officially allows that with subscriptions.
- hakunin 3mo agoYou must've missed the OpenAI's response to Anthropic forcing everyone to their own harness if they want subscription pricing: official endorsement of custom harnesses like opencode and pi even when used with Codex subscription. I think they even partnered with opencode or something like that (don't remember).
- siva7 3mo agoI swear some days ago someone here claimed Openai succeeded cutting down their compute cost by half with a breakthrough optimization. So this is it?
- simonw 3mo agoThat 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-discovers-new-way-cut-inference-costs-half?rc=cpi0u7 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.
- jiggawatts 3mo agoI bet that since this bug has made headlines, there are some panicked engineers at OpenAI desperately trying to figure out how to fix it without undoing their “magic optimisation”.
- brookst 3mo agoIs there any indication that optimization shipped? Depending on whether it’s R&D or pragmatic, I’d expect it to take months at least.
- SyneRyder 3mo agoMy 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.
- nsingh2 3mo agoOh this seems bad, and is fairly easy to reproduce using codex cli. You give it a puzzle prompt that it has to reason about and solve, occasionally it will seemingly short circuit and think for exactly 516 tokens, and return the wrong result. When it ends up using 6000-8000 thinking tokens it returns the correct result. Maybe some issue with adaptive thinking? Another point for local models I guess, don't have to worry about silent server side changes. Edit: To follow up, it seems to happen quite often. Out of 10 runs of the exact same prompt, 4/10 had this 516 thinking token issue, and every one of these had the wrong solution. So nearly half the time, 5.5 xhigh could be short circuiting and degrading performance. Granted the sample size is small.
- deleted 3mo ago[deleted]
- dannyw 3mo agoI wonder if testing during different time/days show patterns? For example, whether the short circuiting happens more often during workday peak hours.
- postalcoder 3mo agoYou still have to worry about misconfigured local models. Even the professionals get it wrong, which is why local model performance is uneven across providers.
- deleted 3mo ago[deleted]
- jdiff 3mo agoBut in that case you have nobody but yourself to blame, and you can stabilize things yourself at any time by refraining from making any changes. You won't be surprised by a provider. Honestly? That's not just valuable—it's essential.
- LiamPowell 3mo ago
- ACCount37 3mo agoA rare case "they made the model dumber" where they actually made the model dumber, instead of the usual user psychosis?
- perching_aix 3mo agoIt 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.
- ACCount37 3mo ago"They made the model dumber" on literally the same checkpoint with the same prompt on the same quantization running on the same hardware is a staple of AI complaints. Users are completely incapable of objectively evaluating model quality over time. Which makes it all the harder to notice actual "stealth nerfs", misconfigurations or other technical issues. Because "they made the model DUMBER, for REAL this time" is background noise.
- dannyw 3mo agoHow are you so sure that frontier API models are always running the same quant/weights/etc? You think OpenAI and Anthropic are running essentially just vLLM endpoints? Of course not. Firstly, we know Anthropic has been doing prompt injection into their 1P APIs (not bedrock/vertex AFAIK) for at least a year now. https://old.reddit.com/r/ClaudeAI/comments/1f6hcwo/injections_in_the_api/ https://old.reddit.com/r/ClaudeAI/comments/1f6hcwo/injection... This can be verified pretty quickly like OP — count the token metrics, if your context contains classifier-firing terms, you’ll see input_tokens being higher than your input. So if they’re already doing that, what makes you think it’s just a dumb API, instead of a complicated pipeline filled with trade secrets and optimisations?
- resonious 3mo agoDeja 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.
- jatora 3mo agoThe 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.
- HumanOstrich 3mo agoIf you bother to look at the issue instead of whining and complaining, you will see the evidence.
- sigbottle 3mo agoWhen I disagree with the data: I will nitpick every last detail of methodology, any cross-corroboration is an anecdote, suddenly I demand a-priori levels of justification. All science is flawed anyways, it's not like mathematics, you can't get absolute certainty, so why bother? You're always going to be making base assumptions that can be challenged, you're necessarily going to abstract out the territory, the map is flawed. When I agree with the data: I will boast about the victories of science and empiricism, we found the perfect set of natural abstractions that are necessary and sufficient to map out the territory that carve at the joints of the problem, any concern about assumptions is rebutted with generic "Well, we're just pragmatists; we're not perfect, but clearly we're converging on the right direction! You're clearly someone who just wants to nitpick and not get any work done." My experience with certain hackernews commenters in a nutshell.
- jatora 3mo agoThis is evidence of a bug, not the purposeful enshittification people are referencing
- trycaedral 3mo ago[flagged]
- vitorgrs 3mo agoIt's been a month I've been using it as they gave me for free, and I found GPT-5 on Codex quite weird/awful. Even x-high. Then I figured out I should try OMP (Pi), and the experience was much better. I remember GPT 5.2 Codex being fine...
- ghosty141 3mo agoMaybe 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.
- keyle 3mo agoThey rendered 5.3 unusable for me a few weeks back. It simply was locking up or answering poorly.
- ifwinterco 3mo agoIt’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
- notfried 3mo ago5.3 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.
- jiggawatts 3mo agoDoes this affect the Codex app too, or just the Codex CLI tool?
- nsingh2 3mo agoFrom 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.
- jiggawatts 3mo agoIf this really is widespread and degrading performance in 40% of the cases, then if OpenAI simultaneously fixes this bug and releases GPT 5.6 within a day or two, then the sudden boost in capability is going to blow people's hair back.
- kordlessagain 3mo agoSounds like a problem with promoting the drafter.
- linzhangrun 3mo agoThe good experience I had with GPT-5.5 before made me upgrade to Pro this month. Now I want a refund.
- cbg0 3mo agoYou 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.
- linzhangrun 3mo agoNo, the decline in GPT-5.5's performance over the past few weeks is clearly noticeable.
- cbg0 3mo agoDoesn't look like it: https://marginlab.ai/trackers/codex/ https://marginlab.ai/trackers/codex/
- mdgld 3mo agoCool resource and perfect way to track this, thanks for sharing
- linzhangrun 3mo agoThanks for sharing this project. Maybe I'm being subjective.
- memoriyato3 3mo agoits called hedonic adaptation - you get excited by a new model, but then the excitement disappears, and you confuse that with the model being nerfed
- rahidz 3mo agoSo what are we to make of the two items: - This tracker not showing any visible degradation. - Clearly incorrect answers being reported due to truncated thinking. Is the tracker not measuring 'simpler' tasks that might get auto-sent to "low reasoning hell" even on high/xhigh? Is the clustering not actually causing reasoning misses in real-life coding, or not enough of a negative effect compared to the improvements made elsewhere? Something else?
- wahnfrieden 3mo agoReset!
- laurels-marts 3mo agoI love that Codex is open source and issues like these can surface/be addressed publicly.
- adithyassekhar 3mo agoI feel openai in general is much more open and real business like compared to anthropic. They’re just a black box.
- rockwotj 3mo agoBut 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 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
- zuzululu 3mo agothis 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: codex 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. " 1. 516, 24 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.
- cageface 3mo agoVerified this locally myself. Thanks for the concrete test. I guess it's time to give Claude another try.
- zuzululu 3mo agoI would switch to Claude if they kept Fable 5 in the sub I'm also afraid to lose my "spot" if I leave codex and 5.6 is coming out so...
- nsingh2 3mo agoThis is preliminary, but it seems like it might somehow be related to the `## Intermediary updates` system prompt that's provided to the model. Seems like it forces the model to stop thinking and return early to provide updates. Removing that entirely makes all runs succeed [1]. I wonder if it's somehow getting confused between what's supposed to be an intermediate update vs the final result. [1] https://github.com/openai/codex/issues/30364#issuecomment-4886510189 https://github.com/openai/codex/issues/30364#issuecomment-48...
- edg5000 3mo agoFor 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.
- chazeon 3mo agoEven without stats i know it went bad. 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. And it show how far we are from AGI.
- LPisGood 3mo ago> 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.
- cbg0 3mo agoThis 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?
- chazeon 3mo agoWell, when 5.5 first came out, it was kind of OK, but now it's almost noticeably worse. It can be done, but requires a lot of effort, just more and more round, and actually, the Gemini Pro on the web (which should be 3.1 Pro) is actually doing a more stable job. The thing that I ask it to do is like take X and Y paper into Z paragraph --- a not-so-silly model should think of how information in X and Y are related and how they support the whole article to synthesize this sentence in a way that is coherent to the article, but 5.5 now will just copy the stuff without any reasoning about the relation. Of course, this will cost a lot of tokens and will be obvious if not done. One clear indicator is that in a few rounds you can see the length of the article get bloated to 2-3x undesirably long, which is clearly because it is not analyzing/synthesizing the info.
- dualdust 3mo ago[flagged]
- preetham_rangu 3mo agoI swear all these ai companies are trying to rob us for more price
- inigyou 3mo agoI have some bad news: every company is trying to rob you for more price
- openclawclub 3mo ago[dead]
- joohwan 3mo agoI'm seeing this issue with 5.4 also.
- maxignol 3mo agoThis seems really bad…
- AmazingTurtle 3mo agoIt's funny, they sell you a subscription for frontier models, then over time begin to nerf them rapidly and no one talks about it. Should give me a discount when they reduce reasoning effort silently on the server side! But on the other hand, I've been using 5.5-high on a daily basis in multithreading workflows, i.e. in parallel. I'm barely exhausting my weekly limits. I can't even Human-as-a-Service fast enough to catch up and read all the plans and implementations it does. So there is that.
- benjiro29 3mo ago> they sell you a subscription for frontier models, then over time begin to nerf them rapidly and no one talks about it. People talk about it all the time. Just check some of the dozens of forums where its non-stop complaining about nerfs, limit nerfs, performance issues etc... Is hard to prove that any downgrade is a effect of being deliberately served a lower class model / lower quant, or whatever. Or the "optimizations" hurting the models performance. The TOS allows for those service "optimizations", so legally, nobody has a foot to stand upon. Like when OpenAI or was it Anthropic played with the cache, this to free up more server resources, only to later discover that its gutted the long term context behavior, and heavily degraded the models as context grew. If you want 100% guaranteed the same performance/behavior, you need to run a model yourself (be it rented GPUs online or your own local setup). But its going to cost you a lot more ...
- brookst 3mo agoI’ve been paying $200/mo for Claude code for, IDK, 9 months? I am 100% sure that I get far more value from today than I did in December. The models are smarter, the limits are higher. It’s possible there’s some “five steps forward, one step back” going on, but it’s hard to imagine complaining about that step back.
- josephernest 3mo agoYou 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()
- deleted 3mo ago[deleted]
- tyingq 3mo ago> reasoning-token clustering at 516/1034/1552 Interesting. So 516 probably means initial 512 byte buffer and a 4 byte header. Then 516 + 518 = 1034...so another 512 + 4 byte header + 2 bytes for a linked list ref or similar, 1034 + 518 = 1552, etc.
- ComputerGuru 3mo agoAlready reported (not as thoroughly but still quite detailed) two weeks ago and silently “closed as not planned” (keep in mind that the specific reason might be an artifact of GitHub workflow/UX and not actually the intended reason) without a acknowledgement or a response. https://github.com/openai/codex/issues/29353 https://github.com/openai/codex/issues/29353 What even is the point of a public-facing bug tracker “for devs, by devs” when this is how reports get treated? Might as well use Apple’s Feedback Reporter that routes to /dev/null instead. Anyway, I find it near impossible to see how this wasn’t already caught and flagged internally – it’s not a subtle pattern. Certainly they are at the very least collecting and graphing reasoning tokens vs model vs effort” and such an obvious spike at (multiple) single stops (not even distributed over a narrow range) should have been an immediate statistical red flag… which leads me to believe (combined with the fact the previously reported issue was closed without comment) that they’re at least internally aware of this behavior even if it’s not necessarily an intentional side effect of some internal forcing metric.
- rq1 3mo agoI was wondering WTF was happening. This was past month: 516 + 518*n 516 n=0 count=4454 1034 n=1 count=318 1552 n=2 count=129 2070 n=3 count=56 2588 n=4 count=35 3106 n=5 count=14 3624 n=6 count=6 4142 n=7 count=4 4660 n=8 count=6
- yetanotherjosh 3mo agoThere is nothing called "GPT5.5 Codex" unless I've completely misunderstood OpenAI's product line? Codex is a harness, while GPT-5.5 is a model. The last codex-branded model was 5.3. Codex as a harness ships as a CLI, a desktop app, and a web product (and I'm not at all sure how similar the underlying harness is between them.) Is the bug here supposed to be with the CLI harness, or the model? Does it also happen in pi, opencode, etc while running GPT-5.5?
- geophph 3mo agoIf the issue comments are to be believed it could be related to the codex system prompt itself so likely just the harness. I agree the wording is weird but they appear to be referencing GPT 5.5 in Codex because yeah there isn’t a GPT5.5 Codex model.
- m3h 3mo agoIndeed, it looks like my work has suffered from the clustering issue as well: reasoning_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 1.1792 Created a script for this: https://github.com/thehappybug/codex-reasoning-token-check https://github.com/thehappybug/codex-reasoning-token-check
- m3h 3mo agoWhen 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.
- nsingh2 3mo agoI think it might affect real work if part of it requires a lot of thinking, i.e. something similar in nature to a puzzle. There seems to be something wrong with the "commentary" channel related intermediate updates, maybe the model gets confused about what's an intermediate update vs what's the final answer? [1] [1] https://github.com/openai/codex/issues/30364#issuecomment-4886390844 https://github.com/openai/codex/issues/30364#issuecomment-48...
- redml 3mo agoif these ai companies want to be taken seriously as being productivity tools then they're going to have to stop with these ab tests and forcing unproven features onto everyone. it's bad enough that ais are inherently unpredictable in quality of output, but these kinds of changes just make things worse. anthropic at least does have a latest and stable channel, as the other day they pushed something irritating that would skip question asking phase if you didn't reply in 60 seconds, and it broke my multi terminal workflow. like I don't know what their product people are thinking when they push this kind of stuff, but it made me switch to stable
- LogicFailsMe 3mo agoOh cool, another source of LLM nondeterminism. Just what we needed!
- nickalaso 3mo agoSo, I have done a bit of research on this with the writeup+skills+scripts on my personal git: https://github.com/NickalasLight/codex-reasoning-bug-512-token/blob/main/README.md https://github.com/NickalasLight/codex-reasoning-bug-512-tok... I think it is very interesting that: A. Removing the section in question seems to greatly fix performance on the benchmark candy question. B. Removing the section does not appear to change at all mean reasoning token use or the 512 reasoning token hit problem
- DanielLestrange 3mo agoThe issue affects _all_ models (inclusive the "hidden" guardian codex-auto-review model), and _only_ happens on the PRO plans (both 5x and 20x). It does not, under any circumstances, happen with the PLUS plan. There is a probably related difference of CTX windows being different (and significantly larger on PLUS) between the plans, as well as an ominous and not-user-controllable flag `use_responses_lite` which is set to true on PLUS. These two discrepancies between the plans may or may not be the cause for the issue. Amending the base prompt shows some improvements using the canary test prompts, NOT so during real world tasks however. The API presents mixed results: from zero failures to 2/5 failures across runs. These results have been taken from deterministic tests and analysis of historic data on both PLUS, PRO and API accounts. More details visible on the related GH issue in comments.