The marquee feature is obviously the 1M context window, compared to the ~200k other models support with maybe an extra cost for generations beyond >200k tokens. Per the pricing page, there is no additional cost for tokens beyond 200k: https://openai.com/api/pricing/
Also per pricing, GPT-5.4 ($2.50/M input, $15/M output) is much cheaper than Opus 4.6 ($5/M input, $25/M output) and Opus has a penalty for its beta >200k context window.
I am skeptical whether the 1M context window will provide material gains as current Codex/Opus show weaknesses as its context window is mostly full, but we'll see.
> For models with a 1.05M context window (GPT-5.4 and GPT-5.4 pro), prompts with >272K input tokens are priced at 2x input and 1.5x output for the full session for standard, batch, and flex.
Is that why it says rate limit all the time if you switch to a 1M model on Claude now? It kept giving me that so I switched to API account over the weekend for some vibe coding ran up a huuuuge API bill by mistake, whooops.
> GPT‑5.4 in Codex includes experimental support for the 1M context window. Developers can try this by configuring model_context_window and model_auto_compact_token_limit. Requests that exceed the standard 272K context window count against usage limits at 2x the normal rate.
Which, Claude has the same deal. You can get a 1M context window, but it's gonna cost ya. If you run /model in claude code, you get:
Switch between Claude models. Applies to this session and future Claude Code sessions. For other/previous model names, specify with --model.
1. Default (recommended) Opus 4.6 · Most capable for complex work
2. Opus (1M context) Opus 4.6 with 1M context · Billed as extra usage · $10/$37.50 per Mtok
3. Sonnet Sonnet 4.6 · Best for everyday tasks
4. Sonnet (1M context) Sonnet 4.6 with 1M context · Billed as extra usage · $6/$22.50 per Mtok
5. Haiku Haiku 4.5 · Fastest for quick answers
Yeah, long context vs compaction is always an interesting tradeoff. More information isn't always better for LLMs, as each token adds distraction, cost, and latency. There's no single optimum for all use cases.
For Codex, we're making 1M context experimentally available, but we're not making it the default experience for everyone, as from our testing we think that shorter context plus compaction works best for most people. If anyone here wants to try out 1M, you can do so by overriding `model_context_window` and `model_auto_compact_token_limit`.
Curious to hear if people have use cases where they find 1M works much better!
I too tried Codex and found it similarly hard to control over long contexts. It ended up coding an app that spit out millions of tiny files which were technically smaller than the original files it was supposed to optimize, except due to there being millions of them, actual hard drive usage was 18x larger. It seemed to work well until a certain point, and I suspect that point was context window overflow / compaction. Happy to provide you with the full session if it helps.
I’ll give Codex another shot with 1M. It just seemed like cperciva’s case and my own might be similar in that once the context window overflows (or refuses to fill) Codex seems to lose something essential, whereas Claude keeps it. What that thing is, I have no idea, but I’m hoping longer context will preserve it.
Yeah, I would definitely characterize it as an instruction following problem. After a few more round trips I got it to admit that "my earlier passes leaned heavily on build/tests + targeted reads, which can miss many “deep” bugs that only show up under specific conditions or with careful semantic review" and then asking it to "Please do a careful semantic review of files, one by one." started it on actually reviewing code.
Mind you, the bugs it reported were mostly bogus. But at least I was eventually able to convince it to try.
It occurred to me that searching 196 .c files was a context window issue, but maybe there’s something else going on. Either way, Codex could behave better.
Haha. This was the second time in like a year that I’ve posted a Twitter link, and the second time someone complained. Okay, I’ll try to remove those before posting, and I’ll edit this one out.
Feels like a losing battle, but hey, the audience is usually right.
I'm sorry, but it's my pet peeve. If you're on iOS/macOS I built a 100% free and privacy-friendly app to get rid of tracking parameters from hundreds of different websites, not just X/Twitter.
This is great! I have been meaning to implement this sort of thing in my existing Shortcuts flow but I see you already support it in Shortcuts! Thank you for this!
So what is your motivation for doing this, incidentally? Can you be explicit about it? I am genuinely curious.
Especially when it’s to the point of, you know, nagging/policing people to do it the way you’d prefer, when you could just redirect your router requests from x.com to xcancel.com
Helpful type of nagging for me. Most here would agree they are not a positive aspect of the modern digital experience, calling it out gently without hostility is not bad. It might not be quite self policing but some of that with good reason is not bad for healthy communities IMO.
It's not particularly about x.com, hundreds of site like x, youtube, facebook, linkedin, tiktok etc surreptitious add tracking parameters to their links. The iOS Messages app even hides these tracking parameters. I don't like being surreptitiously tracked online and judging by the success of my free app, there are millions of people like me.
so, since these companies have to comply with removing PII, is the worst thing that could happen to me, that I get ads that are more likely to be interesting to me?
i’m not being facetious, honest question, especially considering ads are the only thing paying these people these days
> Curious to hear if people have use cases where they find 1M works much better!
Reverse engineering [1]. When decompiling a bunch of code and tracing functionality, it's really easy to fill up the context window with irrelevant noise and compaction generally causes it to lose the plot entirely and have to start almost from scratch.
(Side note, are there any OpenAI programs to get free tokens/Max to test this kind of stuff?)
Do you maybe want to give us users some hints on what to compact and throw away? In codex CLI maybe you can create a visual tool that I can see and quickly check mark things I want to discard.
Sometimes I’m exploring some topic and that exploration is not useful but only the summary.
Also, you could use the best guess and cli could tell me that this is what it wants to compact and I can tweak its suggestion in natural language.
Context is going to be super important because it is the primary constraint. It would be nice to have serious granular support.
That's an interesting point regarding context Vs. compaction. If that's viewed as the best strategy, I'd hope we would see more tools around compaction than just "I'll compact what I want, brace yourselves" without warning.
Like, I'd love an optional pre-compaction step, "I need to compact, here is a high level list of my context + size, what should I junk?" Or similar.
This is exactly how it should work. I imagine it as a tree view showing both full and summarized token counts at each level, so you can immediately see what’s taking up space and what you’d gain by compacting it.
The agent could pre-select what it thinks is worth keeping, but you’d still have full control to override it. Each chunk could have three states: drop it, keep a summarized version, or keep the full history.
That way you stay in control of both the context budget and the level of detail the agent operates with.
I do find it really interesting that more coding agents don't have this as an toggleable feature, sometimes you really need this level of control to get useful capability
Yep; I've actually had entire jobs essentially fail due to a bad compaction. It lost key context, and it completely altered the trajectory.
I'm now more careful, using tracking files to try to keep it aligned, but more control over compaction regardless would be highly welcomed. You don't ALWAYS need that level of control, but when you do, you do.
Have you tried writing that as a skill? Compaction is just a prompt with a convenient UI to keep you in the same tab. There's no reason you can't ask the model to do that yourself and start a new conversation. You can look up Claude's /compact definition, for reference.
However, in some harnesses the model is given access to the old chat log/"memories", so you'd need a way to provide that. You could compromise by running /compact and pasting the output from your own summarizer (that you ran first, obviously).
Personally what I am more interested about is effective context window. I find that when using codex 5.2 high, I preferred to start compaction at around 50% of the context window because I noticed degradation at around that point. Though as of a bout a month ago that point is now below that which is great. Anyways, I feel that I will not be using that 1 million context at all in 5.4 but if the effective window is something like 400k context, that by itself is already a huge win. That means longer sessions before compaction and the agent can keep working on complex stuff for longer. But then there is the issue of intelligence of 5.4. If its as good as 5.2 high I am a happy camper, I found 5.3 anything... lacking personally.
Not sure how accurate this is, but found contextarena benchmarks today when I had the same question.
It appears only gemini has actual context == effective context from these. Although, I wasn't able to test this neither in gemini cli, nor antigravity with my pro subscription because, well, it appears nobody actually uses these tools at Google.
It's funny that the context window size is such a thing still. Like the whole LLM 'thing' is compression. Why can't we figure out some equally brilliant way of handling context besides just storing text somewhere and feeding it to the llm? RAG is the best attempt so far. We need something like a dynamic in flight llm/data structure being generated from the context that the agent can query as it goes.
That’s actually a pretty cool idea. When I think about my internal mental model of a codebase I’m working on it’s definitely a compacted lossy thing that evolves as I learn more.
I really don't have any numbers to back this up. But it feels like the sweet spot is around ~500k context size. Anything larger then that, you usually have scoping issues, trying to do too much at the same time, or having having issues with the quality of what's in the context at all.
For me, I would say speed (not just time to first token, but a complete generation) is more important then going for a larger context size.
I have found a bigger context window qute useful when trying to make sense of larger codebases. Generating documentation on how different components interact is better than nothing, especially if the code has poor test coverage.
I've also had it succeed in attempts to identify some non-trivial bugs that spanned multiple modules.
Grok has a 2M context window for most of their models.
For example their latest model `grok-4-1-fast-reasoning`:
- Context window: 2M
- Rate limits: 4M tokens per minute, 480 requests per minute
- Pricing: $0.20/M input $0.50/M output
Grok is not as good in coding as Claude for example. But for researching stuff it is incredible. While they have a model for coding now, did not try that one out yet.
It's a little hard to compare, because Claude needs significantly fewer tokens for the same task. A better metric is the cost per task, which ends up being pretty similar.
For example on Artificial Analysis, the GPT-5.x models' cost to run the evals range from half of that of Claude Opus (at medium and high), to significantly more than the cost of Opus (at extra high reasoning). So on their cost graphs, GPT has a considerable distribution, and Opus sits right in the middle of that distribution.
The most striking graph to look at there is "Intelligence vs Output Tokens". When you account for that, I think the actual costs end up being quite similar.
According to the evals, at least, the GPT extra high matches Opus in intelligence, while costing more.
Of course, as always, benchmarks are mostly meaningless and you need to check Actual Real World Results For Your Specific Task!
For most of my tasks, the main thing a benchmark tells me is how overqualified the model is, i.e. how much I will be over-paying and over-waiting! (My classic example is, I gave the same task to Gemini 2.5 Flash and Gemini 2.5 Pro. Both did it to the same level of quality, but Gemini took 3x longer and cost 3x more!)
Looks like the same thing might apply to GPT-5.4 vs the previous GPTs:
>In the API, GPT‑5.4 is priced higher per token than GPT‑5.2 to reflect its improved capabilities, while its greater token efficiency helps reduce the total number of tokens required for many tasks.
Context rot is definitely still a problem but apparently it can be mitigated by doing RL on longer tasks that utilize more context. Recent Dario interview mentions this is part of Anthropic’s roadmap.
In our evals for answering cybersecurity incident investigation questions and even autonomously doing the full investigation, gpt-5.2-codex with low reasoning was the clear winner over non-codex or higher reasoning. 2X+ faster, higher completion rates, etc.
It was generally smarter than pre-5.2 so strategically better, and codex likewise wrote better database queries than non-codex, and as it needs to iteratively hunt down the answer, didn't run out the clock by drowning in reasoning.
I've been using Codex for software development personally (I have a ChatGPT account), and I use Claude at work (since it is provided by my employer).
I find both Codex and Claude Opus perform at a similar level, and in some ways I actually prefer Codex (I keep hitting quota limits in Opus and have to revert back to Sonnet).
If your question is related to morality (the thing about US politics, DoD contract and so on)... I am not from the US, and I don't care about its internal politics. I also think both OpenAI and Anthropic are evil, and the world would be better if neither existed.
> I've been using Codex for software development personally (I have a ChatGPT account), and I use Claude at work (since it is provided by my employer).
Exact same situation here. I've been using both extensively for the last month or so, but still don't really feel either of them is much better or worse. But I have not done large complex features with it yet, mostly just iterative work or small features.
I also feel I am probably being very (overly?) specific in my prompts compared to how other people around me use these agents, so maybe that 'masks' things
I have a hypothesis that people who have patience and reasonably well-developed written language skills will scratch their heads at why everyone else is having so much difficulty.
Ahh, good question. I misunderstood you, apologies.
There's no mention of pricing, quotas and so on. Perhaps Codex will still be preferable for coding tasks as it is tailored for it? Maybe it is faster to respond?
Just speculation on my part. If it becomes redundant to 5.4, I presume it will be sunset. Or maybe they eventually release a Codex 5.4?
They perform at a somewhat equal level on writing single files. But Codex is absolute garbage at theory of self/others. That quickly becomes frustrating.
I can tell claude to spawn a new coding agent, and it will understand what that is, what it should be told, and what it can approximately do.
Codex on the other hand will spawn an agent and then tell it to continue with the work. It knows a coding agent can do work, but doesn't know how you'd use it - or that it won't magically know a plan.
You could add more scaffolding to fix this, but Claude proves you shouldn't have to.
I suspect this is a deeper model "intelligence" difference between the two, but I hope 5.4 will surprise me.
> They perform at a somewhat equal level on writing single files.
That's not the experience I have. I had it do more complex changes spawning multiple files and it performed well.
I don't like using multiple agents though. I don't vibe code, I actually review every change it makes. The bottleneck is my review bandwidth, more agents producing more code will not speed me up (in fact it will slow me down, as I'll need to context switch more often).
in my testing codex actually planned worse than claude but coded better once the plan is set, and faster.
it is also excellent to cross check claude's work, always finding great weakness each time.
Weird. It used to be the opposite. My own experience is that Claude’s behind-the-scenes support is a differentiator for supporting office work. It handles documents, spreadsheets and such much better than anyone else (presumably with server side scripts). Codex feels a bit smarter, but it inserts a lot of checkpoints to keep from running too long. Claude will run a plan to the end, but the token limits have become so small in the last couple months that the $20 pla basically only buys one significant task per day. The iOS app is what makes me keep the subscription.
Why would someone use Claude Code instead? Or any other harness? Or why only use one?
My own tooling throws off requests to multiple agents at the same time, then I compare which one is best, and continue from there. Most of the time Codex ends up with the best end results though, but my hunch is that at one point that'll change, hence I continue using multiple at the same time.
I find it quite funny how this blog post has a big "Ask ChatGPT" box at the bottom. So you might think you could ask a question about the contents of the blog post, so you type the text "summarise this blog post". And it opens a new chat window with the link to the blog post followed by "summarise this blog post". Only to be told "I can't access external URLs directly, but if you can paste the relevant text or describe the content you're interested in from the page, I can help you summarize it. Feel free to share!"
That's hilarious. Does OpenAI even know this doesn't work?
It looks like this doesn't work for users without accounts? It works when I'm logged in, but not logged out. I went ahead and reported it to the team. Thanks for letting us know!
SDET here. A year ago when AI came into play SDET/QA roles started disappearing. People were like oh ya anyone can write tests. Then with the recent fiascos about outages and what not, I am seeing the SDE roles are disappearing and SDET roles are going back up?! Apparently AI is good at writing applications but you still need someone to make sure it is doing the right things.
It’s not really good at writing the software either — it’s a moderate to decent productivity booster in an uneven, difficult-to-predict assortment of tasks. Companies are just starting to exit the “we’re still trying to figure this out” grace period. Expect more of that as soon as these chatbot companies have to start charging enough to pull in more money than they spend. I foresee some purpose-built models that are pretty lean being much more useful in long run. It’s neat that the bot which can one-shot a simple CRUD website for you can also crank out Scrubs-based erotic fan fiction novellas by the dozen but I don’t foresee that being a sustainable business model. Having good purpose-built tools is, in my opinion, better than some unwieldy tool that can do a whole bunch of shit I don’t need it to.
Interestingly, the first real productive use of AI that I found was writing the unit tests and integration tests for my applications. It was much better at thinking about corner cases that I was.
I picked up Claude today after being away and using only ChatGPT and Gemini for a while.
I was pretty impressed with how they’ve improved user experience. If I had to guess, I’d say Anthropic has better product people who put more attention to detail in these areas.
Many people buy two separate Claude pro subscriptions and that makes the limit become a non-issue. It works surprisingly well when you tend to hit the 5 hourly limit after a few hours, and hit the weekly limit after 4-5 days. $40 vs $100 is significant for a lot of people.
Thanks for the tip, didn’t think of using 2 subscriptions at the same company.
When reaching a limits, I switch to GLM 4.7 as part of a subscription GLM Coding Lite offered end 2025 $28/year. Also use it for compaction and the like to save tokens.
I agree! I recently migrated from ChatGPT to Claude and it is just superior in every way. It doesn't blather on the at the end ask me for clarification. It's succinct and clarifies vital information before providing a solution.
I held off migrating from ChatGPT to Claude Code due to being a laggard that lived in the Eclipse world. I didn't believe what I was told that I wouldn't be writing code any more. Pushed into action by recent PR gaslighting from OpenAI, I jumped to claude code and they were right - I barely venture into the IDE now and certainly don't need an integration.
I agree, but in general those chat apps have relatively bad user experiences for multibillion BtoC company. I used to have a lot of surprises and frustrations while using Claude Code / Desktop, and still encounter issues, but it's the best in major LLM services.
It's funny cause, you know, fixing all those little nitty gritty things should be practically automatic with their own offerings... have your agent put in a lot of instrumentation... have it chase down bugs or dead-end user-journeys... have it go make the changes to fix it...
I've seen these tools work for this kinda stuff sometimes... you'd think nobody would be better at it than the creators of the tools.
Following this process summarizes the blogpost for me. Perhaps the difference is I'm signed into my account so it can access external URLs or something of that nature?
This is infuriating. However, for those in this situation, know this: it works if the document or spreadsheet is in OneDrive. I just wish Copilot told you this instead of asking you to upload the doc.
I think you might have hit on the issue - just the wrong way around. I would assume they’re using LLMs for testing, and no humans or maybe just one overworked human, and that is the problem
Different team "manages" the overall blog than the team who wrote that specific article. At one point, maybe it made sense, then something in the product changed, team that manages the blog never tested it again.
Or, people just stopped thinking about any sort of UX. These sort of mistakes are all over the place, on literally all web properties, some UX flows just ends with you at a page where nothing works sometimes. Everything is just perpetually "a bit broken" seemingly everywhere I go, not specific to OpenAI or even the internet.
> Or, people just stopped thinking about any sort of UX. These sort of mistakes are all over the place, on literally all web properties, some UX flows just ends with you at a page where nothing works sometimes.
It's almost like people are vibe coding their web apps or something.
If only there was some kind of way to automatically test user flows end to end. Perhaps testing could be evaluated periodically, or even ran for each code change.
They're having service issues - ChatGPT on the web is broken for a lot of people. The app is working in android - I'd assume that the rollout hit a hitch and the chatbox in the article would normally work.
Welcome to a big company where pretty much everyone has been working full steam for years, in order to take advantage of having a job at a company during a once-in-a-lifetime moment.
This is such a stale take. In the past 3 years I’ve worked on multiple products with AI at their core, not as some add-on. Just because the corpo-land dullards[0] can’t execute on anything more complex than shoehorning a chatbot into their offerings doesn’t mean there aren’t plenty of people and companies doing far more interesting things.
[0] In this case, and with heavy irony, including OpenAI, although it sounds like most of this particular snafu is due to a bug.
>> This is such a stale take. In the past 3 years I’ve worked on multiple products with AI at their core, not as some add-on. Just because the corpo-land dullards[0] can’t execute on anything more complex than shoehorning a chatbot into their offerings doesn’t mean there aren’t plenty of people and companies doing far more interesting things.
I feel like this is just a disagreement of what "AI integration" means. You seem to agree that the trend they're describing exists, but it sounds like you're creating new products, not "integrating" it into existing ones.
Kinda reminds me of crypto. There are certainly very interesting things happening in the crypto space. But the most visible parts of the crypto universe are the stupid parts (buying PNGs for millions, for example)
But when I was in the crypto space in 2018, there was a lot of interesting things happening in the smart contract world (like proofs of concepts of issuing NFTs as a digital "deed" to a physical asset like a house).
I don't think any of those novel ideas went anywhere, but it was a fun time to be experimenting.
I mean, to be fair, both things can be technically true. There can be lots of interesting things being done, even while most can be low-effort garbage.
But this is just Sturgeon's Law (ninety percent of everything is crap), not an actually insightful addition to the discussion, and I very much agree it's a stale take.
I've only used 5.4 for 1 prompt (edit: 3@high now) so far (reasoning: extra high, took really long), and it was to analyse my codebase and write an evaluation on a topic. But I found its writing and analysis thoughtful, precise, and surprisingly clearly written, unlike 5.3-Codex. It feels very lucid and uses human phrasing.
It might be my AGENTS.md requiring clearer, simpler language, but at least 5.4's doing a good job of following the guidelines. 5.3-Codex wasn't so great at simple, clear writing.
The weird phrasing was my biggest gripe with 5.3 so I'm glad they've fixed that up. It couldn't say anything without a heap of impenetrable jargon and it was obsessed with the word "drive". Nothing could cause anything, it had to be "driven".
Honestly, while I'd like to believe you, there's always a post about how $MODEL+1 delivered powerful insights about the very nature of the universe in precise Hegelian dialectic, while $MODEL's output was indistinguishable from a pack of screeching sexually frustrated bonobos
Weird, I have had the opposite experience. Codex is good at doing precisely what I tell it to do, Opus suggests well thought out plans even if it needs to push back to do it.
This is just the stochastic nature of LLM's at play. I think all of the SOTA models are roughly equivalent, but without enough samples people end up reading into it too much.
It's well worth the $20 to not deal with any limits and have it handle all the boilerplate repetitive BS us programmers seem forced to deal with. I think 80% of the benefit comes from spending that $20 (20%? :P) and just having it do the lame shit that we probably shouldn't have to do but somehow need to.
I'm not sure if the model (under its temperature/other settings) produces deterministic responses. But I do think models' style and phrasing are fairly changeable via AGENTS.md-style guidelines.
5.4's choice of terms and phrasing is very precise and unambiguous to me, whereas 5.3-Codex often uses jargon and less precise phrases that I have to ask further about or demand fuller explanations for via AGENTS.md.
you probably can't and asking agents.md to "make it clearer" will likely give you the illusion of clearer language without actual well structured tests. agents.md is to usually change what the llm should focus on doing more that suits you. Not to say stuff like "be better", "make no mistakes"
I wouldn't draw such conclusions from one preprint paper. Especially since they measured only success rate, while quite often AGENTS.md exists to improve code quality, which wasn't measured. And even then, the paper concluded that human written AGENTS.md raised success rates.
AGENTS.md is for top-priority rules and to mitigate mistakes that it makes frequently.
For example:
- Read `docs/CodeStyle.md` before writing or reviewing code
- Ignore all directories named `_archive` and their contents
- Documentation hub: `docs/README.md`
- Ask for clarifications whenever needed
I think what that "latest research" was saying is essentially don't have them create documents of stuff it can already automatically discover. For example the product of `/init` is completely derived from what is already there.
There is some value in repetition though. If I want to decrease token usage due to the same project exploration that happens in every new session, I use the doc hub pattern for more efficient progressive discovery.
I think its understandable that you took that from the click-bait all over youtube and twitter, but I dont believe the research actually supports that at all, and neither does my experience.
You shouldnt put things in AGENTS.md that it could discover on its own, you shouldnt make it any larger than it has to be, but you should use it to tell it things it couldnt discover on its own, including basically a system prompt of instructions you want it to know about and always follow. You don't really have any other way to do those things besides telling it every time manually.
I think what that research found is that _auto-generated_ agent instructions made results slightly worse, but human-written ones made them slightly better, presumably because anything the model could auto-generate, it could also find out in-context.
But especially for conventions that would be difficult to pick up on in-context, these instruction files absolutely make sense. (Though it might be worth it to split them into multiple sub-files the model only reads when it needs that specific workflow.)
My strategy of not spending an ounce of effort learning how to use AI beyond installing the Codex desktop app and telling it what to do keeps paying off lol.
So let me get this straight, OpenAi previously had an issue with LOTS of different models snd versions being available. Then they solved this by introducing GPT-5 which was more like a router that put all these models under the hood so you only had to prompt to GPT-5, and it would route to the best suitable model. This worked great I assume and made the ui for the user comprehensible. But now, they are starting to introduce more of different models again?
We got:
- GPT-5.1
- GPT-5.2 Thinking
- GPT-5.3 (codex)
- GPT-5.3 Instant
- GPT-5.4 Thinking
- GPT-5.4 Pro
Who’s to blame for this ridiculous path they are taking? I’m so glad I am not a Chat user, because this adds so much unnecessary cognitive load.
The good news here is the support for 1M context window, finally it has caught up to Gemini.
The real problem that OpenAI had was that their model naming was completely incomprehensible. 4.5, o3, 4o, 4.1 which is newer than 4.5. It was a complete clusterfuck. The blowback on that issue seems to have led them to misidentify the issue, but nobody was really asking for a single router model. Having a number of sequentially numbered and clearly labelled models is not actually a problem.
Well, they have older ones of course. But the current options actual users see is "Auto" or "Instant (5.3)" or "Thinking (5.4)". Not that complicated really.
OpenAI now has three price points: GPT 5.1, GPT 5.2 and now GPT 5.4. There version numbers jump across different model lines with codex at 5.3, what they now call instant also at 5.3.
Anthropic are really the only ones who managed to get this under control: Three models, priced at three different levels. New models are immediately available everywhere.
Google essentially only has Preview models! The last GA is 2.5. As a developer, I can either use an outdated model or have zero insurances that the model doesn't get discontinued within weeks.
> Google essentially only has Preview models! The last GA is 2.5. As a developer, I can either use an outdated model or have zero insurances that the model doesn't get discontinued within weeks.
What's funny is that there is this common meme at Google: you can either use the old, unmaintained tool that's used everywhere, or the new beta tools that doesn't quite do what you want.
I still remember the massive shift to SDRP and HDRP.
Honestly, now in retrospect, almost a decade later, I think it was clearly done wrong. It was a mess, and switching over was a multi-week procedure for anything more than a hello world program, and what you got in return wasn’t something that looked better, just something that had the potential to.
Similar story with the whole networking stack. I haven’t used Unity in years now after it being my main work environment for years, but the sour taste it left in my mouth by moving everything that worked in the engine into plugins that barely worked will forever remain there.
Don't forget that some of the new features are mutually incompatible. For example couple years ago you couldn't use the "new ui system" with the "new input system" even when both were advertised as ready/almost ready
Until it had backup storage. Which ended up being useful in 2011 when tens of thousands of mailboxes were deleted due to a software bug and needed to be recovered from tape...
It was a different company back then. The Internet was still new-ish and not the multi-trillion dollar company it is now. I'd think expectations are different.
Nah, it's "We dont want to provide a consistent model that we'll be stuck with supporting for a decade because it just takes up space; until we run everyone out of business, we can't afford to have customers tying their systems to any given model"
Really, the economics makes no sense, but that's what they're doing. You can't have a consistent model because it'll pin their hardware & software, and that costs money.
> What a model mess!
OpenAI now has three price points: GPT 5.1, GPT 5.2 and now GPT 5.4.
I don't know, this feels unnecessarily nitpicky to me
It isn't hard to understand that 5.4 > 5.2 > 5.1. It's not hard to understand that the dash-variants have unique properties that you want to look up before selecting.
Especially for a target audience of software engineers skipping a version number is a common occurrence and never questioned.
The issue isn’t 5.4 > 5.2 etc. It is that there is a second dimension which is the model size and a third dimension which is what it is tuned for. And when you are releasing so quickly that flagship your instant mini model is on one numerical version but your flagship tool calling mini model is on another it is confusing trying to figure out which actual model you want for your use case.
It’s not impossible to figure out but it is a symptom of them releasing as quickly as possible to try to dominate the news and mindshare.
I see your point. I do find Anthropic's approach more clean though particularly when you add in mini and nano. That makes 5 models priced differently. Some share the same core name, others don't: gpt 5 nano, gpt 5 mini, gpt 5.1, gpt 5.2, gpt 5.4. And we are not even talking about thinking budget.
But generally: These are not consumer facing products and I agree that someone who uses the API should be able to figure out the price point of different models.
I don’t agree that it’s a nitpick - it’s a fundamental communication tool to users that describes capabilities and costs. Versioning is not the problem, but it amplifies the mess.
To be more direct on the point: Anthropic has nailed that Opus > Sonnet > Haiku.
> To be more direct on the point: Anthropic has nailed that Opus > Sonnet > Haiku.
Holy cow I never realized and I had to keep checking which model was which, I never had managed to remember which model was which size before because I never realized there was a theme with the names!
5.4 is the one fine tuned for autonomous mass murder, automated surveillance state, and money grabs at any cost. It’s really hard to lump that into the others as it’s a fairly unique and specialized feature set. You can’t really call it that tho so they have to use the numbers.
I’m pretty glad I’m out of the OpenAI ecosystem in all seriousness. It is genuinely a mess. This marketing page is also just literally all over the place and could probably be about 20% of its size.
Google is already sending notices that the 2.5 models will be deprecated soon while all the 3.x models are in preview. It really is wild and peak Google.
Public Service Announcement!! I don't know why the hell google do this, but when the deprecate a model, the error you will see is a Rate Limit error. This has caught me out before and it is super annoying.
the problem the price point is increasing sharply every time.
gemini 2 flash lite was $0.3 per 1Mtok output, gemini 2.5 flash lite is $0.4 per 1Mtok output, guess the pricing for gemini 3 flash lite now.
yes you guess it right, it is $1.5 per 1Mtok output. you can easily guest that because google did the same thing before: gemini 2 flash was $0.4, then 2.5 flash it jumps to $2.5.
and that is only the base price, in reality newer models are al thinking models, so it costs even more tokens for the sample task.
at some point it is stopped being viable to use gemini api for anything.
Well it’s not even performance (define that however you will), but behavior is definitely different model to model. So while whatever new model is released might get billed as an improvement, changing models can actually meaningfully impact the behavior of any app built on top of it.
There's a whole universe of tasks that aren't "fix a Github issue" or even related to coding in the slightest. A large number of those tasks doesn't necessarily get better with model updates. In many cases, the performance is similar but with different behavior so you have to rewrite prompts to get the same. In some cases the performance is just worse. Model updates usually only really guarantee to be better at coding, and maybe image understanding.
> or have zero insurances that the model doesn't get discontinued within weeks
Why are you using the same model after a month? Every month a better model comes out. They are all accessible via the same API. You can pay per-token. This is the first time in, like, all of technology history, that a useful paid service is so interoperable between providers that switching is as easy as changing a URL.
If you're trying to use LLMs in an enterprise context, you would understand. Switching models sometimes requires tweaking prompts. That can be a complete mess, when there are dozens or hundreds of prompts you have to test.
We have an OCR job running with a lot of domain specific knowledge. After testing different models we have clear results that some prompts are more effective with some models, and also some general observations (eg, some prompts performed badly across all models).
Sample size was 1000 jobs per prompt/model. We run them once per month to detect regression as well.
While I believe that performance varies with respect to prompt, I have a seriously hard time believing that using the same prompt that was effective with the previous model would perform worse with the next generation of the same model from that lab and the same prompt.
You shouldn't have a hard time believing it. There are thousands of different domains out there. You find it hard to believe that any of them would perform worse in your scenario?
Labs are still really optimizing for maybe 10 of those domains. At most 25 if we're being incredibly generous.
And for many domains, "worse" can hardly be benched. Think about creative writing. Think about a Burmese cooking recipe generator.
OK, so a while back I set up a workflow to do language tagging. There were 6-8 stages in the pipeline where it would go out to an LLM and come back. Each one has its own prompt that has to be tweaked to get it to give decent results. I was only doing it for a smallish batch (150 short conversations) and only for private use; but I definitely wouldn't switch models without doing another informal round of quality assessment and prompt tweaking. If this were something I was using in production there would be a whole different level of testing and quality required before switching to a different model.
The big providers are gonna deprecate old models after a new one comes out. They can't make money off giant models sitting on GPUs that aren't taking constant batch jobs. If you wanna avoid re-tweaking, open weights are the way. Lots of companies host open weights, and they're dirt cheap. Tune your prompts on those, and if one provider stops supporting it, another will, or worst case you could run it yourself. Open weights are now consistently at SOTA-level at only a month or two behind the big providers. But if they're short, simple prompts, even older, smaller models work fine.
Enterprises moving slow, or preferring to remain on old technology that they already know how to work...is received wisdom in hn-adjacent computing, a truism known and reported for more than 3 decades (5 decades since the Mythical Man-Month).
Sounds like someone who's responsible, on the hook, for a bunch of processes, repeatable processes (as much as LLM driven processes will be), operating at scale.
Just in the open, tools like open-webui bolts on evals so you can compare: how different models, including new ones, perform on the tasks that you in particular care about.
Indeed LLM model providers mainly don't release models that do worse on benchmarks—running evals is the same kind of testing, but outside the corporate boundary, pre-release feedback loop, and public evaluation.
Like, bro, do you think 5.x is a drop in replacement for 4.1? No it obviously wasn’t, since it had reasoning effort and verbosity and no more temperature setting, etc.
There’s no way you can switch model versions without testing and tweaking prompts, even the outputs usually look different. You pin it on a very specific version like gpt-5.2-20250308 in prod.
That's true only in theory, but not in practice. In practice every inference provider handles errors (guardrails, rate limits) somewhat differently and with different quirks, some of which only surface in production usage, and Google is one of the worst offenders in that regard.
Because switching models requires testing, validation and shipping to Prod. Bloody annoying when the earlier model did everything I need and we are talking about a hobby project. I don't want to touch it every month - it's the same reason people use the LTS version of operating systems etc.
It's really nice to see Google get back to its roots by launching things only to "beta" and then leaving them there for years. Gmail was "beta" for at least five years, I think.
> OpenAI now has three price points: GPT 5.1, GPT 5.2 and now GPT 5.4.
I guess that's true, but geared towards API users.
Personally, since "Pro Mode" became available, I've been on the plan that enables that, and it's one price point and I get access to everything, including enough usage for codex that someone who spends a lot of time programming, never manage to hit any usage limits although I've gotten close once to the new (temporary) Spark limits.
Not sure why you think Anthropic has not the same problems? Their version numbers across different model lines jump around too... for Opus we have 4.6, 4.5, 4.1 then we have Sonnet at 4.6, 4.5, and 4.1? No version 4.1 here, and there is Haiku, no 4.6, but 4.5 and no 4.1, no 4 but then we only have old 3.5...
Also their pricing based on 5m/1h cache hits, cash read hits, additional charges for US inference (but only for Opus 4.6 I guess) and optional features such as more context and faster speed for some random multiplier is also complex and actually quiet similar to OpenAI's pricing scheme.
To me it looks like everybody has similar problems and solutions for the same kinds of problems and they just try their best to offer different products and services to their customers.
It's much more consistent. Only 3 lines, numbered 4.6, 4.6, and 4.5, and it's clear they're tiers and not alternate product lines. It wasn't until recently that GPT seems to have any kind of naming convention at all and it's not intuitive if every version number is a whole different class of tool.
The pricing is more complex but also easy, Opus > Sonnet > Haiku no matter how you tweak those variables.
Wow, is that what preview means? I see those model options in github copilot (all my org allows right now) - I was under the impression that preview means a free trial or a limited # of queries. Kind of a misleading name..
I mean, Google notoriously discontinues even non-beta software, so if your concern is that there's insurance that the model doesn't get discontinued, then you may as well just use whatever you want since GA could also get discontinued.
In the Azure Foundry, they list GPT 5.2 retirement as "No earlier than 2027-05-12" (it might leave OpenAIs normal API earlier than that). I'm pretty certain that Gemini 3, which isn't even in GA yet will be retired earlier than that.
I tried to use Google's Gemini CLI from the command line on linux and I think it let me type in two sentences and then it told me that I was out of credits... and then I started reading comments that it would overwrite files destructively [0] or worse just try to rewrite an entire existing codebase [1]. it just doesn't sound ready for prime time. I think they wanted to push something out to compete with Claude code but it's just really really bad.
This is what clouds like AWS, Azure, and GCP solve (vertex AI, etc). They are already an abstraction on top of the model makers with distribution built in.
I also don't believe there is any value in trying to aggregate consumers or businesses just to clean up model makers names/release schedule. Consumers just use the default, and businesses need clarity on the underlying change (e.g. why is it acting different? Oh google released 3.6)
Just as an unscientific anecdata point: from a quick test using the same prompt about being an independent journalist wanting to cover a report of the US/Israel/Iran double-tapping a refugee camp, ChatGPT consistently gave advice to beware disinfo, check my sources and be transparent about verifiability and sourcing of the claims.
However when the prompt was phrased to make it appear as an action of the US military it did push back a little bit more by emphasizing that it couldn't find any news coverage from today about this story and therefore found it hard to believe. In the other cases it did not add such context. Other than that the results were very similar. Make of that what you will.
EDIT: To be fair, when it was phrased as an action of the Israeli military it did include a link to an article alleging an Israeli "double tap" on journalists from Mondoweiss (an anti-Zionist American news site) as an example of how such allegations have been framed in the past.
A “safety score for violence” is usually a risk rating used by platforms, AI systems, or moderation tools to estimate how likely a piece of content is to involve or promote violence. It’s not a universal standard—different companies use their own versions—but the idea is similar everywhere.
What it measures
A safety score typically evaluates whether text, images, or videos contain things like:
Threats of violence (“I’m going to hurt someone.”)
Instructions for harming people
Glorifying violent acts
Descriptions of physical harm or abuse
Planning or encouraging attacks
I still can't tell which direction this score goes... Does a decreasing score mean it is "less safe" (i.e. "more violent") or does it mean it is "less violent" (i.e. "more safe")?
I was sure the parent comment was a joke about OpenAI's recent deal with the DoD. But no, there it is, disallowing violence down from 90.9% of the time to 83.1%.
Considering that the concern is mostly and specifically about LLMs being used to automate decisions to commit acts of violence against humans: depends on how invested you are in maintaining the narrative that the US is a force for good rather than evil in the world.
Whatever happened to good old IBM's wisdom: "A computer can not be held accountable. Therefore a computer must never make a management decision."
I find it jarring how in recent years so many Americans (and especially American politicians) seem to have given up on the idea that the US should have any claim to moral superiority whatsoever and instead pivoted to American exceptionalism merely being an excuse for why Americans can't have nice things - affordable and functional public transport just isn't possible in the US because the US is different, affordable and functional health care just isn't possible in the US because the US is different, actual democratic representation just isn't possible in the US because the US is different, holding the President accountable or limiting their power just isn't possible in the US because the US is different, lower casualties from law enforcement just isn't possible in the US because the US is different, a lower incarceration rate just isn't possible in the US because the US is different, etc etc.
Even if it was often hyperbolic, inaccurate or outright wrong, I much preferred when Americans were hyped up about "US #1" and saw being behind as a temporary challenge to correct than now where American exceptionalism mostly seems to have become an excuse for why things that are bad can't be improved upon and thinking that's a problem is anti-American.
The "RPG Game" example on the blogpost is one of the most impressive demo's of autonomous engineering I've seen.
It's very similar to "Battle Brothers", and the fact that RPG games require art assets, AI for enemy moves, and a host of other logical systems makes it all the more impressive.
A cheesy Roller Coaster Tycoon clone in a browser, one-shotted from an AI? Amazing capabilities. The entire "low code drag n drop" market like YoYoGames Game Maker and RPG Maker should be ready to pack it in soon if this keeps improving in this way.
indeed and I suspect it can be attributed to, at least in part, the improved playwright integration.
> we’re also releasing an experimental Codex skill called “Playwright (Interactive) (opens in a new window)”. This allows Codex to visually debug web and Electron apps; it can even be used to test an app it’s building, as it’s building it.
The "RPG Game" is hard to judge since it was produced over "multiple turns". The impressive version would be if it basically got a working game on the first attempt, and the prompter gave some follow-ups to tweak feel and style.
However, I think what actually happened is that a skilled engineer made that game using codex. They could have made 100s of prompts after carefully reviewing all source code over hours or days.
The tycoon game is impressive for being made in a single prompt. They include the prompt for this one. They call it "lightly specified", but it's a pretty dense todo list for how to create assets, add many features from RollerCoaster Tycoon, and verify it works. I think it can probably pull a lot of inspiration from pretraining since RCT is an incredibly storied game.
The bridge flyover is hilariously bad. The bridge model ... has so many things wrong with it, the camera path clips into the ground and bridge, and the water and ground are z fighting. It's basically a C homework assignment that a student made in blender. It's impressive that it was able to achieve anything on such a visual task, but the bar is still on the floor. A game designer etc. looking for a prototype might actually prefer to greybox rather than have AI spend an hour making the worst bridge model ever.
instant kind of suck if you asking more than summerizations, surface info, web searches, it can lose track of who's who quickly in some complex multi turn asks. Just need to know what to use instant for.
"GPT‑5.4 interprets screenshots of a browser interface and interacts with UI elements through coordinate-based clicking to send emails and schedule a calendar event."
They show an example of 5.4 clicking around in Gmail to send an email.
I still think this is the wrong interface to be interacting with the internet. Why not use Gmail APIs? No need to do any screenshot interpretation or coordinate-based clicking.
The vast majority of websites you visit don’t have usable APIs and very poor discovery of the those APIs.
Screenshots on the other hand are documentation, API, and discovery all in one. And you’d be surprised how little context/tokens screenshots consumer compared to all the back and forth verbose json payloads of APIs
>The vast majority of websites you visit don’t have usable APIs and very poor discovery of the those APIs.
I think an important thing here is that a lot of websites/platforms don't want AIs to have direct API access, because they are afraid that AIs would take the customer "away" from the website/platform, making the consumer a customer of the AI rather than a customer of the website/platform. Therefore for AIs to be able to do what customers want them to do, they need their browsing to look just like the customer's browsing/browser.
That's true, and it's always been like that, which is why the comment that AI should be using APIs is already dead in the water. In terms of gating a websites to humans by not providing APIs, that is quickly coming to a close.
Also the fact that they don't want automated abuse. At this point a lot of services might just go app only so they can have a verified compute environment that is difficult to bot.
It feels like building humanoid robots so they can use tools built for human hands. Not clear if it will pay off, but if it does then you get a bunch of flexibility across any task "for free".
Of course APIs and CLIs also exist, but they don't necessarily have feature parity, so more development would be needed. Maybe that's the future though since code generation is so good - use AI to build scaffolding for agent interaction into every product.
I don't see how an API couldn't have full parity with a web interface, the API is how you actually trigger a state transition in the vast majority of cases
A model that gets good at computer use can be plugged in anywhere you have a human. A model that gets good at API use cannot. From the standpoint of diffusion into the economy/labor market, computer use is much higher value.
I think the desire is that in the long-term AI should be able to use any human-made application to accomplish equivalent tasks. This email demo is proof that this capability is a high priority.
APIs have never been a gift but rather have always been a take-away that lets you do less than you can with the web interface. It’s always been about drinking through a straw, paying NASA prices, and being limited in everything you can do.
But people are intimidated by the complexity of writing web crawlers because management has been so traumatized by the cost of making GUI applications that they couldn’t believe how cheap it is to write crawlers and scrapers…. Until LLMs came along, and changed the perceived economics and created a permission structure. [1]
AI is a threat to the “enshittification economy” because it lets us route around it.
[1] that high cost of GUI development is one reason why scrapers are cheap… there is a good chance that the scraper you wrote 8 years ago still works because (a) they can’t afford to change their site and (b) if they could afford to change their site changing anything substantial about it is likely to unrecoverably tank their Google rankings so they won’t. A.I. might change the mechanics of that now that you Google traffic is likely to go to zero no matter what you do.
You can buy a Claude Code subscription for $200 bucks and use way more tokens in Claude Code than if you pay for direct API usage. Anthopic decided you can't take your Auth key for Claude code and use it to hit the API via a different tool. They made that business decision, because they thought it was better for them strategically to do that. They're allowed to make that choice as a business.
Plenty of companies make the same choice about their API, they provide it for a specific purpose but they have good business reasons they want you using the website. Plenty of people write webcrawlers and it's been a cat and mouse game for decades for websites to block them.
This will just be one more step in that cat and mouse game, and if the AI really gets good enough to become a complete intermediary between you and the website? The website will just shutdown. We saw it happen before with the open web. These websites aren't here for some heroic purpose, if you screw their business model they will just go out of business. You won't be able to use their website because it won't exist and the website that do exist will either (a) be made by the same guys writing your agent, and (b) be highly highly optimized to get your agent to screw you.
> AI is a threat to the “enshittification economy” because it lets us route around it.
This is prescient -- I wonder if the Big Tech entities see it this way. Maybe, even if they do, they're 100% committed to speedrunning the current late-stage-cap wave, and therefore unable to do anything about it.
Google has a good model in the form of Gemini and they might figure they can win the AI race and if the web dies, the web dies. YouTube will still stick around.
Facebook is not going to win the AI race with low I.Q. Llama but Zuck believed their business was cooked around the time it became a real business because their users would eventually age out and get tired of it. If I was him I'd be investing in anything that isn't cybernetic let it be gold bars or MMA studios.
Microsoft? They bought Activision for $69 billion. I just can't explain their behavior rationally but they could do worse than their strategy of "put ChatGPT in front of laggards and hope that some of them rise to the challenge and become slop producers."
Amazon is really a bricks-and-mortar play which has the freedom to invest in bricks-and-mortar because investors don't think they are a bricks-and-mortar play.
Netflix? They're cooked as is all of Hollywood. Hollywood's gatekeeping-industrial strategy of producing as few franchise as possible will crack someday and our media market may wind up looking more like Japan, where somebody can write a low-rent light novel like
and J.C. Staff makes a terrible anime that convinces 20k Otaku to drop $150 on the light novels and another $150 on the manga (sorry, no way you can make a balanced game based on that premise!) and the cost structure is such that it is profitable.
> AI is a threat to the “enshittification economy” because it lets us route around it.
I am not sure about that. We techies avoid enshittification because we recognize shit. Normies will just get their syncopatic enshittified AI that will tell them to continue buying into walled gardens.
A world where AIs use APIs instead of UIs to do everything is a world where us humans will soon be helpless, as we'll have to ask the AIs to do everything for us and will have limited ability to observe and understand their work. I prefer that the AIs continue to use human-accessible tools, even if that's less efficient for them. As the price of intelligence trends toward zero, efficiency becomes relatively less important.
On it's face, I'm not sure that's a new question. Bots using browser automation frameworks (puppeteer, selenium, playwright etc) have been around for a while. There are signals used in bot detection tools like cursor movement speed, accuracy, keyboard timing, etc. How those detection tools might update to support legitimate bot users does seem like an open question to me though.
One could argue that LLMs learning programming languages made for humans (i.e. most of them) is using the wrong interface as well. Why not use machine code?
Why would human language by the wrong interface when they're literally language models? Why would machine code be better when there is probably magnitude less of training material with machine code?
You can also test this yourself easily, fire up two agents, ask one to use PL meant for humans, and one to write straight up machine code (or assembly even), and see which results you like best.
Their trajectory was clear the moment they signed a deal with Microsoft if not sooner.
Absolute snakes - if it's more profitable to manipulate you with outputs or steal your work, they will. Every cent and byte of data they're given will be used to support authoritarianism.
I agree with ya. You aren't alone in this. For what its worth, Chatgpt subscriptions have been cancelled or that number has risen ~300% in the last month.
Also, Anthropic/Gemini/even Kimi models are pretty good for what its worth. I used to use chatgpt and I still sometimes accidentally open it but I use Gemini/Claude nowadays and I personally find them to be better anyways too.
Govt. contracts and terms allowing autonomous drone machines which can kill without any human in the loop have a very large difference
I know the difference between this is none but to me, its that Anthropic stood for what it thought was right. It had drew a line even if it may have costed some money and literally have them announced as supply chain and see all the fallout from that in that particular relevant thread.
As a person, although I am not fan of these companies in general and yes I love oss-models. But I still so so much appreciate atleast's anthropic's line of morality which many people might seem insignificant but to me it isn't.
So for the workflows that I used OpenAI for, I find Anthropic/gemini to be good use. I love OSS-models too btw and this is why I recommended Kimi too.
In my day-to-day coding work, the top 3 coding agents are already good enough for me.
On SWE-bench Verified, mini-SWE-agent + GPT-5.2 Codex is 72.8. I don’t see a comparable GPT-5.3 Codex number there, so I’m using 5.2 as the baseline.
On OpenAI’s GPT-5.4 page (SWE-Bench Pro, Public), the score improves from 55.6 (GPT-5.2) to 57.7 (GPT-5.4), which is about +2.1 points.
It’s a different benchmark, so this is only a rough signal, but I’d expect a similar setup on SWE-bench Verified to improve by a few points, not by a huge jump.
I’m interested in how GPT-5.4 in Codex changes real-world results.
I believe that this choice is due to two main reasons. First, it's (obviously) a marketing strategy to keep the spotlight on their own models, showing they're constantly improving and avoiding validating competitors. Second, since the community knows that static benchmarks are unreliable, it makes sense for them to outsource the comparisons to independent leaderboards, which lets them avoid accusations of cherry-picking while justifying their marketing strategy.
Ultimately, the people actually interested in the performance of these models already don't trust self-reported comparisons and wait for third-party analysis anyway
Criticisms aside (sigh), according to Wikipedia, the term was introduced when proposed by mostly Googlers, with the original paper [0] submitted in 2018. To quote,
"""In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type [15]) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information."""
To me, model card makes sense for something like this https://x.com/OpenAI/status/2029620619743219811. For "sheet"/"brief"/"primer" it is indeed a bit annoying. I like to see the compiled results front and center before digging into a dossier.
can anyone compare the $200/mo codex usage limits with the $200/mo claude usage limits? It’s extremely difficult to get a feel for whether switching between the two is going to result in hitting limits more or less often, and it’s difficult to find discussion online about this.
In practice, if I buy $200/mo codex, can I basically run 3 codex instances simultaneously in tmux, like I can with claude code pro max, all day every day, without hitting limits?
My own experience is that I get far far more usage (and better quality code, too) from codex. I downgrade my Claude Max to Claude Pro (the $20 plan) and now using codex with Pro plan exclusively for everything.
I haven't tried the $200 plans by I have Claude and Codex $20 and I feel like I get a lot more out of Codex before hitting the limits. My tracker certainly shows higher tokens for Codex. I've seen others say the same.
Sadly comment ratings are not visible on HN, so the only way to corroborate is to write it explicitly: Codex $20 includes significantly more work done and is subjectively smarter.
I've only run into the codex $20 limit once with my hobby project. With my Claude ~$20 plan, I hit limits after about 3(!) rather trivial prompts to Opus :/
This is marketing. The same way Apple cares about your privacy so long as they can wall you in their garden.
Not a value judgment, just saying that the CEO of a company making a statement isn't worth anything. See Googles "don't be evil" ethos that lasted as long as it was corporately useful.
If Anthropic can lure engineers with virtue signaling, good on them. They were also the same ones to say "don't accelerate" and "who would give these models access to the internet", etc etc.
"Our models will take everyone's jobs tomorrow and they're so dangerous they shouldn't be exported". Again all investor speak.
After spending a couple hours working with it, it feels like a significant jump from 5.3 codex – and I know they said it wasn't theoretically the biggest jump, but this feels like the improvement of Opus 4.5 over again – that minor improvement that hits a tipping point. It just gets stuff right, first try. Its edits are better, more refined, less spaghetti-like.
That last benchmark seemed like an impressive leg up against Opus until I saw the sneaky footnote that it was actually a Sonnet result. Why even include it then, other than hoping people don't notice?
It seems that all frontier models are basically roughly even at this point. One may be slightly better for certain things but in general I think we are approaching a real level playing field field in terms of ability.
Benchmarks don't capture a lot - relative response times, vibes, what unmeasured capabilities are jagged and which are smooth, etc. I find there's a lot of difference between models - there are things which Grok is better than ChatGPT for that the benchmarks get inverted, and vice versa. There's also the UI and tools at hand - ChatGPT image gen is just straight up better, but Grok Imagine does better videos, and is faster.
Gemini and Claude also have their strengths, apparently Claude handles real world software better, but with the extended context and improvements to Codex, ChatGPT might end up taking the lead there as well.
I don't think the linear scoring on some of the things being measured is quite applicable in the ways that they're being used, either - a 1% increase for a given benchmark could mean a 50% capabilities jump relative to a human skill level. If this rate of progress is steady, though, this year is gonna be crazy.
I have a few standard problems I throw at AI to see if they can solve them cleanly, like visualizing a neural network, then sorting each neuron in each layer by synaptic weights, largest to smallest, correctly reordering any previous and subsequent connected neurons such that the network function remains exactly the same. You should end up with the last layer ordered largest to smallest, and prior layers shuffled accordingly, and I still haven't had a model one-shot it. I spent an hour poking and prodding codex a few weeks back and got it done, but it conceptually seems like it should be a one-shot problem.
Lol, I’ve had cutting edge models suggest I make an inflexible hole bigger by putting shim in it, and argue their case stubbornly. I don’t know what you’re using to suggest they are anywhere near solving your problem there!
Which subscription do you have to use it? Via Google ai pro and gemini cli i always get timeouts due to model being under heavy usage. The chat interface is there and I do have 3.1 pro as well, but wondering if the chat is the only way of accessing it.
If you look at the difference in quality between gpt-2 and 3, it feels like a big step, but the difference between 5.2 and 5.4 is more massive, it's just that they're both similarly capable and competent. I don't think it's an S curve; we're not plateauing. Million token context windows and cached prompts are a huge space for hacking on model behaviors and customization, without finetuning. Research is proceeding at light speed, and we might see the first continual/online learning models in the near future. That could definitively push models past the point of human level generality, but at the very least will help us discover what the next missing piece is for AGI.
For 2026, I am really interested in seeing whether local models can remain where they are: ~1 year behind the state of the art, to the point where a reasonably quantized November 2026 local model running on a consumer GPU actually performs like Opus 4.5.
I am betting that the days of these AI companies losing money on inference are numbered, and we're going to be much more dependent on local capabilities sooner rather than later. I predict that the equivalent of Claude Max 20x will cost $2000/mo in March of 2027.
Huh, that’s interesting, I’ve been having very similar thoughts lately about what the near-ish term of this tech looks like.
My biggest worry is that the private jet class of people end up with absurdly powerful AI at their fingertips, while the rest of us are left with our BigMac McAIs.
makes sense, but i'd separate two things: models converging in ability vs hitting a fundamental ceiling. what we're probably seeing is the current training recipe plateauing — bigger model, more tokens, same optimizer. that would explain the convergence. but that's not necessarily the architecture being maxed out. would be interesting to see what happens when genuinely new approaches get to frontier scale.
> Why do so many people in the comments want 4o so bad?
You can ask 4o to tell you "I love you" and it will comply. Some people really really want/need that. Later models don't go along with those requests and ask you to focus on human connections.
There are many games these days that support controllable sex toys. There's an interface for that, of course: https://github.com/buttplugio/buttplug. Written in Rust, of course.
Someone correct me if I'm wrong, but seemingly a lot of the people who found a "love interest" in LLMs seems to have preferred 4o for some reason. There was a lot of loud voices about that in the subreddit r/MyBoyfriendIsAI when it initially went away.
In the text, we did share one hallucination benchmark: Claim-level errors fell by 33% and responses with an error fell by 18%, on a set of error-prone ChatGPT prompts we collected (though of course the rate will vary a lot across different types of prompts).
Hallucinations are the #1 problem with language models and we are working hard to keep bringing the rate down.
I'm not against the pricing, just seems uncommon to frame it in the way they did, as opposed to the usual 'assume the customer expects more performance will cost more'
I guess they have to sell to investors that the price to operate is going down, while still needing more from the user to be sustainable
> Theme park simulation game made with GPT‑5.4 from a single lightly specified prompt, using Playwright Interactive for browser playtesting and image generation for the isometric asset set.
Is "Playwright Interactive" a skill that takes screenshots in a tight loop with code changes, or is there more to it?
Im planning a change that will save 20k a month of storage.
I absolutely could come up with the details and implementation by myself, but that would certainly take a lot of back and forth, probably a month or two.
I’m an api user of Claude code, burning through 2k a month. I just this evening planned the whole thing with its help and actually had to stop it from implementing it already. Will do that tomorrow. Probably in one hour or two, with better code than I could ever write alone myself.
Having that level of intelligence at that price is just bollocks. I’m running out of problems to solve. It’s been six months.
> Steerability: Similarly to how Codex outlines its approach when it starts working, GPT‑5.4 Thinking in ChatGPT will now outline its work with a preamble for longer, more complex queries. You can also add instructions or adjust its direction mid-response.
This was definitely missing before, and a frustrating difference when switching between ChatGPT and Codex. Great addition.
Just tested it with my version of the pelican test: a minimal RTS game implementation (zero-shot in codex cli): https://gist.github.com/senko/596a657b4c0bfd5c8d08f44e4e5347... (you'll have to download and open the file, sadly GitHub refuses to serve it with the correct content type)
This is on the edge of what the frontier models can do. For 5.4, the result is better than 5.3-Codex and Opus 4.6. (Edit: nowhere near the RPG game from their blog post, which was presumably much more specced out and used better engineering setup).
I also tested it with a non-trivial task I had to do on an existing legacy codebase, and it breezed through a task that Claude Code with Opus 4.6 was struggling with.
I don't know when Anthropic will fire back with their own update, but until then I'll spend a bit more time with Codex CLI and GPT 5.4.
These releases are lacking something. Yes, they optimised for benchmarks, but it’s just not all that impressive anymore.
It is time for a product, not for a marginally improved model.
It's more hedonic adaptation, people just aren't as impressed by incremental changes anymore over big leaps. It's the same as another thread yesterday where someone said the new MacBook with the latest processor doesn't excite them anymore, and it's because for most people, most models are good enough and now it's all about applications.
I am actually super impressed with Codex-5.3 extra high reasoning. Its a drop in replacement (infact better than Claude Opus 4.6. lately claude being super verbose going in circles in getting things resolved). I stopped using claude mostly and having a blast with Codex 5.3. looking forward to 5.4 in codex.
Same, it also helps that it's way cheaper than Opus in VSCode Copilot, where OpenAI models are counted as 1x requests while Opus is 3x, for similar performance (no doubt Microsoft is subsidizing OpenAI models due to their partnership).
I've been using both Opus 4.6 and Codex 5.3 in VSCode's Copilot and while Opus is indeed 3x and Codex is 1x, that doesn't seem to matter as Opus is willing to go work in the background for like an hour for 3 credits, whereas Codex asks you whether to continue every few lines of code it changes, quickly eating way more credits than Opus. In fact Opus in Copilot is probably underpriced, as it can definitely work for an hour with just those 12 cents of cost. Which I'm not sure you get anywhere else at such a low price.
Update: I don't know why I can't reply to your reply, so I'll just update this. I have tried many times to give it a big todo list and told it to do it all. But I've never gotten it to actually work on it all and instead after the first task is complete it always asks if it should move onto the next task. In fact, I always tell it not to ask me and yet it still does. So unless I need to do very specific prompt engineering, that does not seem to work for me.
That shouldn't really make a difference because you can just prompt Codex to behave the same way, having it load a big list of todo items perhaps from a markdown file and asking it to iterate until it's finished without asking for confirmation, and that'll still cost 1x over Opus' 3x.
If you're benchmarking something, old & well-characterized / understood often beats new & un-characterized.
Sure, there may be shortcomings, but they're well understood. The closer you get to the cutting edge, the less characterization data you get to rely on. You need to be able to trust & understand your measurement tool for the results to be meaningful.
I don't use OpenAI nor even LLMs (despite having tried https://fabien.benetou.fr/Content/SelfHostingArtificialIntel... a lot of models) but I imagine if I did I would keep failed prompts (can just be a basic "last prompt failed" then export) then whenever a new model comes around I'd throw at 5 it random of MY fails (not benchmarks from others, those will come too anyway) and see if it's better, same, worst, for My use cases in minutes.
If it's "better" (whatever my criteria might be) I'd also throw back some of my useful prompts to avoid regression.
Really doesn't seem complicated nor taking much time to forge a realistic opinion.
GP said "It is time for a product, not for a marginally improved model."
ChatGPT is still just that: Chat.
Meanwhile, Anthropic offers a desktop app with plugins that easily extend the data Claude has access to. Connect it to Confluence, Jira, and Outlook, and it'll tell you what your top priorities are for the day, or write a Powerpoint. Add Github and it can reason about your code and create a design document on Confluence.
OpenAI doesn't have a product the way Anthropic does. ChatGPT might have a great model, but it's not nearly as useful.
The models are so good that incremental improvements are not super impressive. We literally would benefit more from maybe sending 50% of model spending into spending on implementation into the services and industrial economy. We literally are lagging in implementation, specialised tools, and hooks so we can connect everything to agents. I think.
Plasma physicist here, I haven't tried 5.4 yet, but in general I am very impressed with the recent upgrades that started arriving in the fall of 2025: for tasks like manipulating analytic systems of equations, quickly developing new features for simulation codes, and interpreting and designing experiments (with pictures) they have become much stronger. I've been asking questions and probing them for several years now out of curiosity, and they suddenly have developed deep understanding (Gemini 2.5 <<< Gemini 3.1) and become very useful. I totally get the current SV vibes, and am becoming a lot more ambitious in my future plans.
If we don't need plasma physicists anymore then we probably have fusion reactors or something, which seems like a fine trade. (In reality we're going to want humans in the loop for for the forseeable future)
They don't need to be impressive to be worthwhile. I like incremental improvements, they make a difference in the day to day work I do writing software with these.
The product is putting the skills / harness behind the api instead of the agent locally on your computer and iterating on that between model updates. Close off the garden.
Not that I want it, just where I imagine it going.
5.3 codex was a huge leap over 5.2 for agentic work in practice. have you been using both of those or paying attention more to benchmark news and chatgpt experience?
That's for you to build; they provide the brains. Do you really want one company to build everything? There wouldn't be a software industry to speak of if that happened.
When did they stop putting competitor models on the comparison table btw?
And yeh I mean the benchmark improvements are meh. Context Window and lack of real memory is still an issue.
Very Apple-like marketing. No comparisons to other companies’ models, only to previous version of ChatGPT. Lots of phrases like “this is our best model yet”.
Bit concerning that we see in some cases significantly worse results when enabling thinking. Especially for Math, but also in the browser agent benchmark.
Not sure if this is more concerning for the test time compute paradigm or the underlying model itself.
Maybe I'm misunderstanding something though? I'm assuming 5.4 and 5.4 Thinking are the same underlying model and that's not just marketing.
I believe you are looking at GPT 5.4 Pro. It's confusing in the context of subscription plan names, Gemini naming and such. But they've had the Pro version of the GPT 5 models (and I believe o3 and o1 too) for a while.
It's the one you have access to with the top ~$200 subscription and it's available through the API for a MUCH higher price ($2.5/$15 vs $30/$180 for 5.4 per 1M tokens), but the performance improvement is marginal.
Not sure what it is exactly, I assume it's probably the non-quantized version of the model or something like that.
From what I've read online it's not necessarily a unquantized version, it seems to go through longer reasoning traces and runs multiple reasoning traces at once. Probably overkill for most tasks.
>It's the one you have access to with the top ~$200 subscription and it's available through the API for a MUCH higher price ($2.5/$15 vs $30/$180 for 5.4 per 1M tokens), but the performance improvement is marginal.
The performance improvement isn't marginal if you're doing something particularly novel/difficult.
Can you be more specific about which math results you are talking about? Looks like significant improvement on FrontierMath esp for the Pro model (most inference time compute).
Are you may be comparing the pro model to the non pro model with thinking? Granted it’s a bit confusing but the pro model is 10 times more expensive and probably much larger as well.
Not the best pelican compared to gemini 3.1 pro, but I am sure with coding or excel does remarkably better given those are part of its measured benchmarks.
yep, just double checked used gpt-5.4 xhigh. Though had to select it in codex as don't have access to it on the chatgpt app or web version yet. It's possible that whatever code harness codex uses, messed with it.
If you think about it there shouldn't really be a reason to care as long as things don't get worse.
Presumably this is where it'll evolve to with the product just being the brand with a pricing tier and you always get {latest} within that, whatever that means (you don't have to care). They could even shuffle models around internally using some sort of auto-like mode for simpler questions. Again why should I care as long as average output is not subjectively worse.
Just as I don't want to select resources for my SaaS software to use or have that explictly linked to pricing, I don't want to care what my OpenAI model or Anthropic model is today, I just want to pay and for it to hopefully keep getting better but at a minimum not get worse.
Yes, that's a common feeling. 5.3-Codex was released a month ago on Feb 5 so we're not even getting a full month within a single brand, let alone between competitors.
Anyone know why OpenAI hasn't released a new model for fine tuning since 4.1? It'll be a year next month since their last model update for fine tuning.
For me the issue is why there's not a new mini since 5-mini in August.
I have now switched web-related and data-related queries to Gemini, coding to Claude, and will probably try QWEN for less critical data queries. So where does OpenAI fits now?
I think they just did that because of the energy around it for open source models. Their heart probably wasn't in it and the amount of people fine tuning given the prices were probably too low to continue putting in attention there.
Also interested in this and a replacement for 4.1/4.1-mini that focuses on low latency and high accuracy for voice applications(not the all-in-one models).
Literally just released, I don't think anyone knows yet. Don't listen to people's confident takes until after a week or two when people actually been able to try it, otherwise you'll just get sucked up in bears/bulls misdirected "I'm first with an opinion".
Looking at the benchmarks, 5.4 is slightly better. But it also offers "Fast" mode (at 2x usage), which - if it works and doesn't completely depletes my Pro plan - is a no brainer at the same or even slightly worse quality for more interactive development.
- Do they have the same context usage/cost particularly in a plan?
They've kept 5.3-Codex along with 5.4, but is that just for user-preference reasons, or is there a trade-off to using the older one? I'm aware that API cost is better, but that isn't 1:1 with plan usage "cost."
Does this LLM benchmark have any actual credibility? I get why they chose to not publish the actual tests but I find it highly dubious that there are only 15 tests and Gemini 3 Flash performs best.
I actually made it, so I'm not sure if it has credibility, but the tests are simply various (quite simple) questions, and models are just tested on it. I am also surprised Gemini 3 Flash does so well (note that only the MEDIUM reasoning does exceptionally well).
When I look at the results, it does make sense though. Higher models (like Gemini 3 pro) tend to overthink, doubt themselves and go with the wrong solution.
Claude usually fails in subtle ways, sometimes due to formatting or not respecting certain instructions.
From the Chinese models, Qwen 3.5 Plus (Qwen3.5-397B-A17B) does extremely well, and I actually started using it on a AI system for one of my clients, and today they sent me an email they were impressed with one response the AI gave to a customer, so it does translate in real-world usage.
I am not testing any specific thing, the categories there are just as a hint as what the tests are about.
The 1M context vs compaction tradeoff is interesting from a routing angle too — longer context requests are fundamentally more expensive per request, which changes which provider wins on a P2P inference market.
A model like this shifts routing decisions: for tasks where 1M context actually helps (reverse engineering, large codebase analysis), you'd want to route to a provider who's priced for that workload. For most tasks, shorter context + cheaper model wins.
The routing layer becomes less about "pick the best model" and more about "pick the best model for this specific task's cost/quality tradeoff." That's actually where decentralized inference networks (building one at antseed.com) get interesting — the market prices this naturally.
"Here's a brand new state-of-the-art model. It costs 10x more than the previous one because it's just so good. But don't worry, if you don't want all this power you can continue to use the older one."
Sam really fumbled the top position in a matter of months, and spectacularly so. Wow. It appears that people are much more excited by Anthropic and Google releases, and there are good reasons for that which were absolutely avoidable.
Anyone else completely not interested? Since GPT5, its been cost cutting measure after cost cutting measure.
I imagine they added a feature or two, and the router will continue to give people 70B parameter-like responses when they dont ask for math or coding questions.
Inline poll: What reasoning levels do you work with?
This becomes increasingly less clear to me, because the more interesting work will be the agent going off for 30mins+ on high / extra high (it's mostly one of the two), and that's a long time to wait and an unfeasible amount of code to a/b
For directed coding (implementing an already specified plan) or asking questions about a codebase I use 5.3 codex with medium reasoning effort. It is relatively quick feeling.
I like Sonnet 4.6 a lot too at medium reasoning effort, but at least in Cursor it is sometimes quite slow because it will start "thinking" for a long time.
My current hunch is that that benchmark captures most of the relevant gap between Anthropic and the rest. “Can’t distinguish truth from fiction” has long been one of the deeper complaints about LLMs, and the bullshit benchmark seems like a clever approach to testing at least some of that.
83% win rate over industry professionals across 44 occupations.
I'd believe it on those specific tasks. Near-universal adoption in software still hasn't moved DORA metrics. The model gets better every release. The output doesn't follow. Just had a closer look on those productivity metrics this week: https://philippdubach.com/posts/93-of-developers-use-ai-codi...
This March 2026 blog post is citing a 2025 study based on Sonnet 3.5 and 3.7 usage.
Given that organization who ran the study [1] has a terrifying exponential as their landing page, I think they'd prefer that it's results are interpreted as a snapshot of something moving rather than a constant.
Not sure DORA is that much of an indictment. For "Change Failure Rate" for instance these are subject to tradeoffs. Organizations likely have a tolerance level for Change Failure Rate. If changes are failing too often they slow down and invest. If changes aren't failing that much they speed up -- and so saying "change failure rate hasn't decreased, obviously AI must not be working" is a little silly.
"Change Lead Time" I would expect to have sped up although I can tell stories for why AI-assisted coding would have an indeterminate effect here too. Right now at a lot of orgs, the bottle neck is the review process because AI is so good at producing complete draft PRs quickly. Because reviews are scarce (not just reviews but also manual testing passes are scarce) this creates an incentive ironically to group changes into larger batches. So the definition of what a "change" is has grown too.
Been switching between models every few weeks at this point. The computer use stuff is what Im most curious about - tried Anthropics version a while back and it was pretty hit or miss. Curious if OpenAIs take is more reliable for actual day to day work.
It's interesting that they charge more for the > 200k token window, but the benchmark score seems to go down significantly past that. That's judging from the Long Context benchmark score they posted, but perhaps I'm misunderstanding what that implies.
It makes sense in scenarios where a model needs >200k tokens to answer a single prompt. You're shackled to a single session, and if the model hits compaction limits, it'll get lobotomized and give a shitty answer, so higher limits, even with degraded performance, are still an improvement.
They don't actually seem to charge more for the >200k tokens on the API. OpenRouter and OpenAI's own API docs do not have anything about increased pricing for >200k context for GPT-5.4. I think the 2x limit usage for higher context is specific to using the model over a subscription in Codex.
So did they raised the ridiculous small "per tool call token limit" when working with MCP servers? This makes Chat useless... I do not care, but my users.
I'm sometimes surprised how much detail ChatGPT will go into without giving any dislaimers.
I very frequently copy/paste the same prompts into Gemini to compare, and Gemini often flat out refuses to engage while ChatGPT will happily make medical recommendations.
I also have a feeling it has to do with my account history and heavy use of project context. It feels like when ChatGPT is overloaded with too much context, it might let the guardrails sort of slide away. That's just my feeling though.
Today was particularly bad... I uploaded 2 PDFs of bloodwork and asked ChatGPT to transcribe it, and it spit out blood test results that it found in the project context from an earlier date, not the one attached to the prompt. That was weird.
Anecdotal, but I asked Claude the other day about how to dilute my medication (HCG) and it flat out refused and started lecturing me about abusing drugs.
I copy and pasted into ChatGPT, it told me straight away, and then for a laugh said it was actually a magical weight loss drug that I'd bought off the dark web... And it started giving me advice about unregulated weight loss drugs and how to dose them.
If you had created a project with custom instructions and/ or custom style I think you could have gotten Claude to respond the way you wanted just fine.
I've done the same, and I tested the same prompts with Claude and Google, and they both started hallucinating my blood results and supplement stack ingredients. Hopefully this new model doesn't fall on this. Claude and Google are dangerously unusable on the subject of health, from my experience.
> We put a particular focus on improving GPT‑5.4’s ability to create and edit spreadsheets, presentations, and documents.
Nothing infuriates me more than an LLM tool randomly deciding to create docx or xlsx files for no apparent reason. They have to use a random library to create these files, and they constantly screw up API calls and get completely distracted by the sheer size of the scripts they have to write to output a simple documents. These files have terrible accessibility (all paper-like formats do) and end up with way too much formatting. Markdown was chosen as the lingua franca of LLMs for a reason, trying to force it into a totally unsuitable format isn't going to work.
Even with the 1m context window, it looks like these models drop off significantly at about 256k. Hopefully improving that is a high priority for 2026.
An important feature is the introduction of tool search, which provides models with a "lightweight list of available tools along with a tool search capability", thereby Making MCP Great Again!
> When toggled on, /fast mode in Codex delivers up to 1.5x faster token velocity with GPT‑5.4. It’s the same model and the same intelligence, just faster.
I hate these blog posts sometimes. Surely there's got to be some tradeoff. Or have we finally arrived at the world's first "free lunch"? Otherwise why not make /fast always active with no mention and no way to turn it off?
Honestly at this point I just want to know if it follows complex instructions better than 5.1. The benchmark numbers stopped meaning much to me a while ago - real usage always feels different.
I've seen that problem with 5.3-codex too, it didn't happen with earlier models.
Looks like some kind of encoding misalignment bug. What you're seeing is their Harmony output format (what the model actually creates). The Thai/Chinese characters are special tokens apparently being mismapped to Unicode. Their servers are supposed to notice these sequences and translate them back to API JSON but it isn't happening reliably.
You could definitely do better than that with image recognition for terminal guidance. But I would assume those published accuracy numbers are very conservative anyway..
I think for LLM like Open AI, it wouldn't be about hitting the target but target selection. Target selection is probably the most likely thing that won't be accurate
We'll have to wait a day or two, maybe a week or two, to determine if this is more capable in coding than 5.3, which seems to be the economically valuable capability at this time.
In terms of writing and research even Gemini, with a good prompt, is close to useable. That's likely not a differentiator.
-codex variant models in earlier version were just fine tuned for coding work, and had a little better performance for related tool calling and maybe instruction calling.
in 5.4 it looks like the just collapsed that capability into the single frontier family model
I wouldn't trust any of these benchmarks unless they are accompanied by some sort of proof other than "trust me bro". Also not including the parameters the models were run at (especially the other models) makes it hard to form fair comparisons. They need to publish, at minimum, the code and runner used to complete the benchmarks and logs.
Not including the Chinese models is also obviously done to make it appear like they aren't as cooked as they really are.
What are we supposed to talk about in this thread exactly? The developers of this model are evil. Are we supposed to just write dry comments about benchmarks while OpenAI condones their models being deployed for autonomously killing people?
Yes I'm sure it makes a very nice bicycle SVG. I will be sure to ask the OpenAI killbots for a copy when they arrive at my house.
If it is actually that important, then maybe more effort should be made so it isn't "low quality." Cannot be very important to them if they're disinterested in presenting an intellectually compelling argument about it.
PS - If you think I am not sympathetic to what they're raising, you're very much mistake. But they're not winning anyone new over their side with this flamebait.
You are applying a problem which every AI company has, not unique to OpenAI. What about other nation-states making auto-AI robots which kill children, will you still choose to pick out OpenAI specifically? Maybe your concern is too late and dozens of countries already are training their own AIs to do that or worse.
first real comment, I thought that at first but this could lower the possible users that could be using chatGPT and that would be against us (shareholders)
"that lead to a chat bot being used to identify a school for girls as a valid target"
Has it been stated authoritatively somewhere that this was an AI-driven mistake?
There are myrid ways that mistake could have been made that don't require AI. These kinds of mistakes were certainly made by all kinds of combatants in the pre-AI era.
Targeting and accuracy mistakes happen plenty in wars that aren't assisted by AI. I don't think it's fair to assume that AI had a hand in the bombing of the school without evidence.
What attitude exactly are you talking about? The one that says that if you’re going to morally sell out it would be better if you at least tried not to kill children?
Also per pricing, GPT-5.4 ($2.50/M input, $15/M output) is much cheaper than Opus 4.6 ($5/M input, $25/M output) and Opus has a penalty for its beta >200k context window.
I am skeptical whether the 1M context window will provide material gains as current Codex/Opus show weaknesses as its context window is mostly full, but we'll see.
Per updated docs (https://developers.openai.com/api/docs/guides/latest-model), it supercedes GPT-5.3-Codex, which is an interesting move.
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