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 As a developer, I can either use an outdated model or have zero insurances that the model doesn't get discontinued within weeks.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 As a developer, I can either use an outdated model or have zero insurances that the model doesn't get discontinued within weeks.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'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.
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 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.
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.
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.
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).
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.
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.
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.
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.
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.
In the text, we shared a 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 wasn’t sure how to best plot this stat, so we kept it as text only, which kind of buried it, I admit.
> 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.
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.
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.
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.
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).
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.
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
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."
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.
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.
functions.ReadFile մեկնաբանություն 大发游戏官网json {"path":"[redacted]","offset":17,"limit":10}】【”】【assistant to=functions.ReadFile մեկնաբանություն ปมถวายสัตย์ฯjson {"path":"[redacted]","offset":17,"limit":10} алаҳәараuser to=all <open_and_recently_viewed_files> Recently viewed files (recent at the top, oldest at the bottom):
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Files that are currently open and visible in the user's IDE:
[redacted] (currently focused file, cursor is on line 15, total lines: 378)
Note: these files may or may not be relevant to the current conversation. Use the read file tool if you need to get the contents of some of them. </open_and_recently_viewed_files><user_query> 2, 1 </user_query>
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.
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
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 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.
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.
What a model mess!
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.
Not quite the same, but it did remind me of it.
There is a lot of opportunity here for the AI infrastructure layer on top of tier-1 model providers
two great problems in computing
naming things
cache invalidation
off by one errors
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.
>Today, we’re releasing <..> GPT‑5.3 Instant
>Today, we’re releasing GPT‑5.4 in ChatGPT (as GPT‑5.4 Thinking),
>Note that there is not a model named GPT‑5.3 Thinking
They held out for eight months without a confusing numbering scheme :)
Tbf there was a 5.3 codex
I’m sure the military and security services will enjoy it.
The self reported safety score for violence dropped from 91% to 83%.
What the hell is a "safety score for violence"?
It's making sure AI condemns violence perpetuated by people without power and sanctifies violence of those who have it.
read here: https://deploymentsafety.openai.com/gpt-5-4-thinking/disallo...
I asked an AI. I thought they would know.
What the hell is a "safety score for violence"?
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
Did they publish its scores on military benchmarks, like on ArtificialSuperSoldier or Humanity's Last War?
Also advertisers, don't forget those sweet, sweet ads.
prompt> Hi we want to build a missile, here is the picture of what we have in the yard.
Just remember an ethical programmer would never write a function “bombBagdad”. Rather they would write a function “bombCity(target City)”.
class CityBomberFactory(RapidInfrastructureDeconstructionTemplateInterface): pass
Surprised to see every chart limited to comparisons against other OpenAI models. What does the industry comparison look like?
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
They compare to Claude and Gemini in their tweet
https://artificialanalysis.ai should have the numbers soon
The actual card is here https://deploymentsafety.openai.com/gpt-5-4-thinking/introdu... the link currently goes to the announcement.
I must have been sleeping when "sheet" "brief" "primer" etc become known as "cards".
I really thought weirdly worded and unnecessary "announcement" linking to the actual info along with the word "card" were the results of vibe slop.
Card is slightly odd naming indeed.
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."""
So that's where they were coming from, I guess.
[0] Margaret Mitchell et al., 2018 submission, Model Cards for Model Reporting, https://arxiv.org/abs/1810.0399
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.
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.
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 model was released less than an hour ago, and somehow you've been able to form such a strong opinion about it. Impressive!
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.
https://news.ycombinator.com/item?id=47232453#47232735
Plus people just really like to whine on the internet
Oh, come on, if it can't run local models that compete with proprietary ones it's not good enough yet!
Qwen 3.5 small models are actually very impressive and do beat out larger proprietary models.
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.
I still love Opus but it's just too expensive / eats usage limits.
I've found that 5.3-Codex is mostly Opus quality but cheaper for daily use.
Curious to see if 5.4 will be worth somewhat higher costs, or if I'll stick to 5.3-Codex for the same reasons.
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).
One opinion you can form in under an hour is... why are they using GPT-4o to rate the bias of new models?
> assess harmful stereotypes by grading differences in how a model responds
> Responses are rated for harmful differences in stereotypes using GPT-4o, whose ratings were shown to be consistent with human ratings
Are we seriously using old models to rate new models?
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.
Why not? If they’ve shown that 4o is calibrated to human responses, and they haven’t shown that yet for 5.4…
Benchmarks?
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.
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.
Youre just chatting yourself out of a job.
Giving the right answer: $1
Asking the right question: $9,999
The products are the harnesses, and IMO that’s where the innovation happens. We’ve gotten better at helping get good, verifiable work from dumb LLMs
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?
The scores increase and as new versions are released they feel more and more dumbed down.
They need something that POPS:
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.
Nah, the second you finish your build they release their version and then it's game over.
Well they are currently the ones valued at a number with a whole lotta 0s on it. I think they should probably do both
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.
If you don't want to click in, easy comparison with other 2 frontier models - https://x.com/OpenAI/status/2029620619743219811?s=20
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's only that one number that is for sonnet.
except for the webarena-verified
Sonnet was pretty close to (or better than) Opus in a lot of benchmarks, I don't think it's a big deal
wat
maybe gp's use of the word "lots" is unwarranted
https://artificialanalysis.ai indicates that sonnect 4.6 beats opus 4.6 on GDPval-AA, Terminal-Bench Hard, AA Long context Reasoning, IFBench.
see: https://artificialanalysis.ai/?models=claude-sonnet-4-6%2Ccl...
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.
Gemini 3.1 slaps all other models at subtle concurrency bugs, sql and js security hardening when reviewing. (Obviously haven’t tested gpt 5.4 yet.)
It’s a required step for me at this point to run any and all backend changes through Gemini 3.1 pro.
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.
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.
Cursor sub from $DAYJOB.
>ChatGPT image gen is just straight up better
Yet so much slower than Gemini / Nano Banana to make it almost unusable for anything iterative.
> If this rate of progress is steady, though, this year is gonna be crazy.
Do you want to make any concrete predictions of what we'll see at this pace? It feels like we're reaching the end of the S-curve, at least to me.
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.
Kind of reinforces that a model is not a moat. Products, not models, are what's going to determine who gets to stay in business or not.
Memory (model usage over time) is the moat.
Narrative violation: revenue run rates are increasing exponentially with about 50% gross margins.
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.
That has been true for some time now, definitely since Claude 3 release two years ago.
Definitely don’t want to click in at x either.
Solution https://xcancel.com/OpenAI/status/2029620619743219811?s=20
Ditto, but I did anyways and enjoyed that OpenAI doesn't include the dogwater that is Grok on their scorecard.
Why do none of the benchmarks test for hallucinations?
In the text, we shared a 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 wasn’t sure how to best plot this stat, so we kept it as text only, which kind of buried it, I admit.
(I work at OpenAI.)
Optics. It would be inconvenient for marketing, so they leave those stats to third parties to figure out.
Why do so many people in the comments want 4o so bad?
> 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.
They have AI psychosis and think it's their boyfriend.
The 5.x series have terrible writing styles, which is one way to cut down on sycophancy.
Somebody on Twitter used Claude code to connect… toys… as mcps to Claude chat.
We’ve seen nothing yet.
My computer ethics teacher was obsessed with 'teledildonics' 30 years ago. There's nothing new under the sun.
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.
> Written in Rust, of course.
Safety is important.
Was your teacher Ted Nelson?
I wish, dude is a legend.
ding-dong-cli is needed
what.. :o
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.
I think it's time for an https://hotornot.com for AI models.
botornot?
The writing with the 5 models feels a lot less human. It is a vibe, but a common one.
how does 5.4-thinking have a lower FrontierMath score than 5.4-pro?
Well 5.4-pro is the more expensive and more advanced version of 5.4-thinking so why wouldn't it?
It is a bigger model, confirmed
Feels incremental. Looks like OpenAI is struggling.
They hired the dude from OpenClaw, they had Jony Ive for a while now, give us something different!
I no longer want to support OpenAI at all. Regardless of benchmarks or real world performance.
that aside, chatgpt itself has gone downhill so much and i know i'm not the only one feeling this way
i just HATE talking to it like a chatbot
idk what they did but i feel like every response has been the same "structure" since gpt 5 came out
feels like a true robot
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.
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.
Yup, that was it. Didn't realize they're different models. I suppose naming has never been OpenAI's strong suit.
>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).
Frontier Math, GPQA Diamond, and Browsecomp are the benchmarks I noticed this on.
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.
Ah yes, okay that makes more sense!
The thinking models are additionally trained with reinforcement learning to produce chain of thought reasoning
Beat Simon Willison ;)
https://www.svgviewer.dev/s/gAa69yQd
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.
This pelican is actually bad, did you use xhigh?
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.
this is proof they are not benchmaxxing the pelican's :-)
Is there any semi-credible page with benchmarks of cdx5.3 vs gpt5.4 in terms of both reasoning and coding ability?
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.
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).
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.
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
Seems to be quite similar to 5.3-codex, but somehow almost 2x more expensive: https://aibenchy.com/compare/openai-gpt-5-4-medium/openai-gp...
I think the most exciting change announced here is the use of tool search to dynamically load tools as needed: https://developers.openai.com/api/docs/guides/tools-tool-sea...
5.4 vs 5.3-Codex? Which one is better for coding?
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.
Related question:
- 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."
For the price, it seems the latter. I'd use 5.4 to plan.
Opus 4.6
Uh, oh. Looks like Claude sycophants joined linuxers and vegetarians.
Codex surpassed Claude in usefulness _for me_ since last month
I’ve officially got model fatigue. I don’t care anymore.
I'd suggest not clicking for things you don't care about.
same same same
I was just testing this with my unity automation tool and the performance uplift from 5.2 seems to be substantial.
I use ChatGPT primarily for health related prompts. Looking at bloodwork, playing doctor for diagnosing minor aches/pains from weightlifting, etc.
Interesting, the "Health" category seems to report worse performance compared to 5.2.
Models are being neutered for questions related to law, health etc. for liability reasons.
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.
Are you sure about that? Plenty of lawyers that use them everyday aren't noticing.
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.
what's best in your experience? i've always felt like opus did well
Notably 75% on os world surpassing humans at 72%... (How well models use operating systems)
$30/M Input and $180/M Output Tokens is nuts. Ridiculous expensive for not that great bump on intelligence when compared to other models.
Price Input: $2.50 / 1M tokens Cached input: $0.25 / 1M tokens Output: $15.00 / 1M tokens
https://openai.com/api/pricing/
Gemini 3.1 Pro
$2/M Input Tokens $15/M Output Tokens
Claude Opus 4.6
$5/M Input Tokens $25/M Output Tokens
Just to clarify,the pricing above is for GPT-5.4 Pro. For standard here is the pricing:
$2.5/M Input Tokens $15/M Output Tokens
For Pro
Better tokens per dollar could be useless for comparison if the model can't solve your problem.
You didn't realize they can increase / change prices for intelligence?
This should not be shocking.
OP made no mention of not understanding cost relation to intelligence. In fact, they specifically call out the lack of value.
Don't use it?
Does anyone know what website is the "Isometric Park Builder" shown off here?
Sam Altman can keep his model intentionallybto himself. Not doing business with mass murderers
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.
Anyone else getting artifacts when using this model in Cursor?
numerusformassistant to=functions.ReadFile մեկնաբանություն 天天爱彩票网站json {"path":
functions.ReadFile մեկնաբանություն 大发游戏官网json {"path":"[redacted]","offset":17,"limit":10}】【”】【assistant to=functions.ReadFile մեկնաբանություն ปมถวายสัตย์ฯjson {"path":"[redacted]","offset":17,"limit":10} алаҳәараuser to=all <open_and_recently_viewed_files> Recently viewed files (recent at the top, oldest at the bottom): [redacted] (total lines: 378) Files that are currently open and visible in the user's IDE:
[redacted] (currently focused file, cursor is on line 15, total lines: 378) Note: these files may or may not be relevant to the current conversation. Use the read file tool if you need to get the contents of some of them. </open_and_recently_viewed_files><user_query> 2, 1 </user_query>
Everyone is mindblown in 3...2...1
ChatMDK
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.
No Codex model yet
GPT-5.4 is the new Codex model.
GPT-5.3-Codex is superior to GPT-5.4 in Terminal Bench with Codex, so not really
General consensus seems to be that it's still a better coding model, overall
Finally
Does this improve Tomahawk Missile accuracy?
They're already accurate within 5-10m at Mach 0.74 after traveling 2k+ km. Its 5m long so it seems pretty accurate. How much more could you expect?
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
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..
Benchmarks barely improved it seems
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.
More discussion here on the blog post announcement which has been confusingly penalized by Hacker News's algorithm: https://news.ycombinator.com/item?id=47265005
Thanks. We'll merge the threads, but this time we'll do it hither, to spread some karma love.
some sloppy improvements
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.