Whats interesting to me is that these gpt-5.3 and opus-4.6 are diverging philosophically and really in the same way that actual engineers and orgs have diverged philosophically
With Codex (5.3), the framing is an interactive collaborator: you steer it mid-execution, stay in the loop, course-correct as it works.
With Opus 4.6, the emphasis is the opposite: a more autonomous, agentic, thoughtful system that plans deeply, runs longer, and asks less of the human.
that feels like a reflection of a real split in how people think llm-based coding should work...
some want tight human-in-the-loop control and others want to delegate whole chunks of work and review the result
Interested to see if we eventually see models optimize for those two philosophies and 3rd, 4th, 5th philosophies that will emerge in the coming years.
Maybe it will be less about benchmarks and more about different ideas of what working-with-ai means
> With Codex (5.3), the framing is an interactive collaborator: you steer it mid-execution, stay in the loop, course-correct as it works.
> With Opus 4.6, the emphasis is the opposite: a more autonomous, agentic, thoughtful system that plans deeply, runs longer, and asks less of the human.
Ain't the UX is the exact opposite? Codex thinks much longer before gives you back the answer.
I've also had the exact opposite experience with tone. Claude Code wants to build with me, and Codex wants to go off on its own for a while before returning with opinions.
This is most likely an inference serving problem in terms of capacity and latency given that Opus X and the latest GPT models available in the API have always responded quickly and slowly, respectively
Codex now lets you tell the LLM tgings in the middle of its thinking without interrupting it, so you can read the thinking traces and tell it to change course if it's going off track.
That just seems like a UI difference. I've always interrupted claude code added a comment and it's continued without much issue. Otherwise if you just type the message is queued for next. There's no real reason to prefer one over the other except it sounds like codex can't queue messages?
I'm personally 100% convinced (assuming prices stay reasonable) that the Codex approach is here to stay.
Having a human in the loop eliminates all the problems that LLMs have and continously reviewing small'ish chunks of code works really well from my experience.
It saves so much time having Codex do all the plumbing so you can focus on the actual "core" part of a feature.
LLMs still (and I doubt that changes) can't think and generalize. If I tell Codex to implement 3 features he won't stop and find a general solution that unifies them unless explicitly told to. This makes it kinda pointless for the "full autonomy" approach since effecitly code quality and abstractions completely go down the drain over time. That's fine if it's just prototyping or "throwaway" scripts but for bigger codebases where longevity matters it's a dealbreaker.
I'm personally 100% convinced of the opposite, that it's a waste of time to steer them. we know now that agentic loops can converge given the proper framing and self-reflectiveness tools.
Converge towards what though... I think the level of testing/verification you need to have an LLM output a non-trivial feature (e.g. Paxos/anything with concurrency, business logic that isn't just "fetch value from spreadsheet, add to another number and save to the database") is pretty high.
Does the AI agent know what your company is doing right now, what every coworker is working on, how they are doing it, and how your boss will change priorities next month without being told?
If it really knows better, then fire everyone and let the agent take charge. lol
I think it's the opposite. Especially considering Codex started out as a web app that offers very little interactivity: you are supposed to drop a request and let it run automatously in a containerized environment; you can then follow up on it via chat --- no interactive code editing.
Fair I agree that was true of early codex and my perception too.. but today there are two announcements that came out and thats what im referring to.
specifically, the GPT-5.3 post explicitly leans into "interactive collaborator" langauge and steering mid execution
OpenAI post: "Much like a colleague, you can steer and interact with GPT-5.3-Codex while it’s working, without losing context."
OpenAI post: "Instead of waiting for a final output, you can interact in real time—ask questions, discuss approaches, and steer toward the solution"
Claude post: "Claude Opus 4.6 is designed for longer-running, agentic work — planning complex tasks more carefully and executing them with less back-and-forth from the user."
I think there is another philosophy where the agent is domain specific. Not that we have to invent an entirely new universe for every product or business, but that there is a small amount of semi-customization involved to achieve an ideal agent.
I would much rather work with things like the Chat Completion API than any frameworks that compose over it. I want total control over how tool calling and error handling works. I've got concerns specific to my business/product/customer that couldn't possibly have been considered as part of these frameworks.
Whether or not a human needs to be tightly looped in could vary wildly depending on the specific part of the business you are dealing with. Having a purpose-built agent that understands where additional verification needs to occur (and not occur) can give you the best of both worlds.
I usually want the codex approach for code/product "shaping" iteratively with the ai.
Once things are shaped and common "scaling patterns" are well established, then for things like adding a front end (which is constantly changing, more views) then letting the autonomous approach run wild can *sometimes* be useful.
I have found that codex is better at remembering when I ask to not get carried away...whereas claude requires constant reminders.
This kind of sounds like both of them stepping into the other’s turf, to simplify a bit.
I haven’t used Codex but use Claude Code, and the way people (before today) described Codex to me was like how you’re describing Opus 4.6
So it sounds like they’re converging toward “both these approaches are useful at different times” potentially? And neither want people who prefer one way of working to be locked to the other’s model.
> With Opus 4.6, the emphasis is the opposite: a more autonomous, agentic, thoughtful system that plans deeply, runs longer, and asks less of the human.
This feels wrong, I can't comment on Codex, but Claude will prompt you and ask you before changing files, even when I run it in dangerous mode on Zed, I can still review all the diffs and undo them, or you know, tell it what to change. If you're worried about it making too many decisions, you can pre-prompt Claude Code (via .claude/instructions.md) and instruct it to always ask follow up questions regarding architectural decisions.
Sometimes I go out of my way to tell Claude DO NOT ASK ME FOR FOLLOW UPS JUST DO THE THING.
yeah I'm mostly just talking about how they're framing it:
"Claude Opus 4.6 is designed for longer-running, agentic work — planning complex tasks more carefully and executing them with less back-and-forth from the user"
I guess its also quite interesting that how they are framing these projects are opposite from how people currently perceive them and I guess that may be a conscious choice...
I get what you mean now, I like that to be fair, sometimes I want Claude to tell me some architectural options, so I ask it so I can think about what my options are, sometimes I rethink my problem if I like Claudes conclusion.
How can they be diverging, LLMs are built on similar foundations aka the Transformer architecture. Do you mean the training method (RLHF) is diverging?
I read this exact comment with I would say completely the same words several times in X and I would bet my money it's LLM generated by someone who has not even tried both the tools. This AI slop even in the site like this without direct monetisation implications from fake engagement is making me sick...
I am definitely using Opus as an interactive collaborator that I steer mid-execution, stay in the loop and course correct as it works.
I mean Opus asks a lot if he should run things, and each time you can tell it to change. And if that's not enough you can always press esc to interrupt.
Just some anecdata++ here but I found 5.2 to be really good at code review. So I can have something crunched by cheaper models, reviewed async by codex and then re-prompt with the findings from the review. It finds good things, doesn't flag nits (if prompted not to) and the overall flow is worth it for me. Speed loss doesn't impact this flow that much.
I have a `codex-review` skill with a shell script that uses the Codex CLI with a prompt. It tells Claude to use Codex as a review partner and to push back if it disagrees. They will go through 3 or 4 back-and-forth iterations some times before they find consensus. It's not perfect, but it does help because Claude will point out the things Codex found and give it credit.
I don’t use OpenAI too much, but I follow a similar work flow. Use Opus for design/architecture work. Move it to Sonnet for implementation and build out. Then finally over to Gemini for review, QC and standards check. There is an absolute gain in using different models. Each has their own style and way of solving the problem just like a human team. It’s kind of awesome and crazy and a bit scary all at once.
5.2 Codex became my default coding model. It “feels” smarter than Opus 4.5.
I use 5.2 Codex for the entire task, then ask Opus 4.5 at the end to double check the work. It's nice to have another frontier model's opinion and ask it to spot any potential issues.
All shared machine learning benchmarks are a little bit bogus, for a really “machine learning 101” reason: your test set only yields an unbiased performance metric if you agree to only use it once. But that just isn’t a realistic way to use a shared benchmark. Using them repeatedly is kind of the whole point.
But even an imperfect yardstick is better than no yardstick at all. You’ve just got to remember to maintain a healthy level of skepticism is all.
Is an imperfect yardstick better than no yardstick? It reminds me of documentation — the only thing worse than no documentation is wrong documentation.
It's not just over-fitting to leading benchmarks, there's also too many degrees of freedom in how a model is tested (harness, etc). Until there's standardized documentation enabling independent replication, it's all just benchmarketing .
Opus 4.5 still worked better for most of my work, which is generally "weird stuff". A lot of my programming involves concepts that are a bit brain-melting for LLMs, because multiple "99% of the time, assumption X is correct" are reversed for my project. I think Opus does better at not falling into those traps. Excited to try out 5.3
Another day, another hn thread of "this model changes everything" followed immediately by a reply stating "actually I have the literal opposite experience and find competitor's model is the best" repeated until it's time to start the next day's thread.
What amazes me the most is the speed at which things are advancing. Go back a year or even a year before that and all these incremental improvements have compounded. Things that used to require real effort to consistently solve, either with RAGs, context/prompt engineering, have become… trivial. I totally agree with your point that each step along the way doesn’t necessarily change that much. But in the aggregate it’s sort of insane how fast everything is moving.
The denial of this overall trend on here and in other internet spaces is starting to really bother me. People need to have sober conversations about the speed of this increase and what kind of effects it's going to have on the world.
And of course the benchmarks are from the school of "It's better to have a bad metric than no metric", so there really isn't any way to falsify anyone's opinions...
In my personal experience the GPT models have always been significantly better than the Claude models for agentic coding, I’m baffled why people think Claude has the edge on programming.
I think for many/most programmers = 'speed + output' and webdev == "great coding".
Not throwing shade anyone's way. I actually do prefer Claude for webdev (even if it does cringe things like generate custom CSS on every page) -- because I hate webdev and Claude designs are always better looking.
But the meat of my code is backend and "hard" and for that Codex is always better, not even a competition. In that domain, I want accuracy and not speed.
This is the way. People are unfortunately starting to divide themselves into camps on this — it’s human nature we’re tribal - but we should try to avoid turning this into a Yankees Redsox.
Both companies are producing incredible models and I’m glad they have strengths because if you use them both where appropriate it means you have more coverage for important work.
GPT 5.2 codex plans well but fucks off a lot, goes in circles (more than opus 4.5) and really just lacks the breadth of integrated knowledge that makes opus feel so powerful.
Opus is the first model I can trust to just do things, and do them right, at least small things. For larger/more complex things I have to keep either model on extremely short leashes. But the difference is enough that I canceled my GPT Pro sub so I could switch to Claude. Maybe 5.3 will change things, but I also cannot continue to ethically support Sam Altman's business.
Didn't make a difference for me. Though I will say, so far 4.6 is really pissing me off and I might downgrade back to 4.5. It just refuses to listen to what I say, the steering is awful.
How many people are building the same thing multiple times to compare model performance? I'm much more interested in getting the thing I'm building getting built, than than comparing AIs to each other.
ARC AGI 2 has a training set that model providers can choose to train on, so really wouldn't recommend using it as a general measure of coding ability.
A key aspect of ARC AGI is to remain highly resistant to training on test problems which is essential for ARC AGI's purpose of evaluating fluid intelligence and adaptability in solving novel problems. They do release public test sets but hold back private sets. The whole idea is being a test where training on public test sets doesn't materially help.
The only valid ARC AGI results are from tests done by the ARC AGI non-profit using an unreleased private set. I believe lab-conducted ARC AGI tests must be on public sets and taken on a 'scout's honor' basis that the lab self-administered the test correctly, didn't cheat or accidentally have public ARC AGI test data slip into their training data. IIRC, some time ago there was an issue when OpenAI published ARC AGI 1 test results on a new model's release which the ARC AGI non-profit was unable to replicate on a private set some weeks later (to be fair, I don't know if these issues were resolved). Edit to Add: Summary of what happened: https://grok.com/share/c2hhcmQtMw_66c34055-740f-43a3-a63c-4b...
I have no expertise to verify how training-resistant ARC AGI is in practice but I've read a couple of their papers and was impressed by how deeply they're thinking through these challenges. They're clearly trying to be a unique test which evaluates aspects of 'human-like' intelligence other tests don't. It's also not a specific coding test and I don't know how directly ARC AGI scores map to coding ability.
More fundamentally, ARC is for abstract reasoning. Moving blocks around on a grid. While in theory there is some overlap with SWE tasks, what I really care about is competence on the specific task I will ask it to do. That requires a lot of domain knowledge.
As an analogy, Terence Tao may be one of the smartest people alive now, but IQ alone isn’t enough to do a job with no domain-specific training.
Opus was quite useless today. Created lots of globals, statics, forward declarations, hidden implementations in cpp files with no testable interface, erasing types, casting void pointers, I had to fix quite a lot and decouple the entangled mess.
Hopefully performance will pick up after the rollout.
,,GPT‑5.3-Codex is the first model we classify as High capability for cybersecurity-related tasks under our Preparedness Framework , and the first we’ve directly trained to identify software vulnerabilities. While we don’t have definitive evidence it can automate cyber attacks end-to-end, we’re taking a precautionary approach and deploying our most comprehensive cybersecurity safety stack to date. Our mitigations include safety training, automated monitoring, trusted access for advanced capabilities, and enforcement pipelines including threat intelligence.''
While I love Codex and believe it's amazing tool, I believe their preparedness framework is out of date. As it is more and more capable of vibe coding complex apps, it's getting clear that the main security issues will come up by having more and more security critical software vibe coded.
It's great to look at systems written by humans and how well Codex can be used against software written by humans, but it's getting more important to measure the opposite: how well humans (or their own software) are able to infiltrate complex systems written mostly by Codex, and get better on that scale.
In simpler terms: Codex should write secure software by default.
That’s just classical OpenAI trying to make us believe they’re closing on AGI…
Like all « so called » research from them and Anthropic about safety alignment and that their tech is so incredibly powerful that guardrails should be put on them.
>Our mitigations include safety training, automated monitoring, trusted access for advanced capabilities, and enforcement pipelines including threat intelligence.
We'll see. The first two things that they said would move from "emerging tech" to "currently exists" by April 2026 are:
- "Someone you know has an AI boyfriend"
- "Generalist agent AIs that can function as a personal secretary"
I'd be curious how many people know someone that is sincerely in a relationship with an AI.
And also I'd love to know anyone that has honestly replaced their human assistant / secretary with an AI agent. I have an assistant, they're much more valuable beyond rote input-output tasks... Also I encourage my assistant to use LLMs when they can be useful like for supplementing research tasks.
Fundamentally though, I just don't think any AI agents I've seen can legitimately function as a personal secretary.
Also they said by April 2026:
> 22,000 Reliable Agent copies thinking at 13x human speed
And when moving from "Dec 2025" to "Apr 2026" they switch "Unreliable Agent" to "Reliable Agent". So again, we'll see. I'm very doubtful given the whole OpenClaw mess. Nothing about that says "two months away from reliable".
I think they immediately corrected their median timelines for takeoff to 2028 upon releasing the article (I believe there was a math mistake or something initially), so all those dates can probably be bumped back a few months. Regardless, the trend seems fairly on track.
There's still no evidence we'll have any take off. At least in the "Foom!" sense of LLMs independently improving themselves iteratively to substantial new levels being reliably sustained over many generations.
To be clear, I think LLMs are valuable and will continue to significantly improve. But self-sustaining runaway positive feedback loops delivering exponential improvements resulting in leaps of tangible, real-world utility is a substantially different hypothesis. All the impressive and rapid achievements in LLMs to date can still be true while major elements required for Foom-ish exponential take-off are still missing.
If only General Relativity had such an ironclad defense of being as unfalsifiable as Foom Hypothesis is. We could’ve avoided all of the quantum physics nonsense.
making the specifications is still hard, and checking how well results match against specifications is still hard.
i dont think the model will figure that out on its own, because the human in the loop is the verification method for saying if its doing better or not, and more importantly, defining better
This has already been going on for years. It's just that they were using GPT 4.5 to work on GPT 5. All this announcement mean is that they're confident enough in early GPT 5.3 model output to further refine GPT 5.3 based on initial 5.3. But yes, takeoff will still happen because of this recursive self improvement works, it's just that we're already past the inception point.
I remember when AI labs coordinated so they didn't push major announcements on the same day to avoid cannibalizing each other. Now we have AI labs pushing major announcements within 30 minutes.
The labs have fully embraced the cutthroat competition, the arms race has fully shed the civilized facade of beneficient mutual cooperation.
Dirty tricks and underhanded tactics will happen - I think Demis isn't savvy in this domain, but might end up stomping out the competition on pure performance.
Elon, Sam, and Dario know how to fight ugly and do the nasty political boardroom crap. 26 is gonna be a very dramatic year, lots of cinematic potential for the eventual AI biopics.
As long the tactics are legal ( i.e. not corporate espionage, bribes etc), the no holds barred full free market competition is the best thing for the market and the consumers.
> As long the tactics are legal ( i.e. not corporate espionage, bribes etc), the no holds barred full free market competition is the best thing for the market and the consumers.
The implicit assumption here is that we have constructed our laws so skillfully that the only path to win a free market competition is by producing a better product, or that all efforts will be spent doing so. This is never the case. It should be self-evident from this that there is a more productive way for companies to compete and our laws are not sufficient to create the conditions.
in the short term maybe, in the long term it depends how many winners you have. If only two, the market will be a duopoly. Customers will get better AI but will have zero power over the way the AI is produced or consumed (i.e. cO2 emission, ethics, etc will be burnt)
This goes way back. When OpenAI launched GPT-4 in 2023, both Anthropic and Google lined up counter launches (Claude and Magic Wand) right before OpenAI's standard 10am launch time.
I wish they’d just stop pretending to care about safety, other than a few researchers at the top they care about safety only as long as they aren’t losing ground to the competition. Game theory guarantees the AI labs will do what it takes to ensure survival. Only regulation can enforce the limits, self policing won’t work when money is involved.
The last thing I would want is for excessively neurotic bureaucrats to interfere with all the mind-blowing progress we've had in the last couple of years with LLM technology.
Europe is prematurely regarded as having lost the AI race. And yet a large portion of Europe live higher quality lives compared to their American counterparts, live longer, and don't have to worry about an elected orange unleashing brutality on them.
If the world is built on AI infrastructure (models, compute, etc.) that is controlled by the CCP then the west has effectively lost.
This may lead to better life outcomes, but if the west doesn't control the whole stack then they have lost their sovereignty.
This is already playing out today as Europe is dependent on the US for critical tech infrastructure (cloud, mail, messaging, social media, AI, etc). There's no home grown European alternatives because Europe has failed to create an economic environment to assure its technical sovereignty.
Europe has already lost the tech race - their cloud systems that their entire welfare states rely upon are all hosted on servers hosted by American private companies, which can turn them off with a flick of a switch if and when needed.
When the welfare state, enabled by technology, falls apart, it won't take long for European society to fall apart. Except France maybe.
I've always been fascinated to see significantly more people talking about using Claude than I see people talking about Codex.
I know that's anecdotal, but it just seems Claude is often the default.
I'm sure there are key differences in how they handle coding tasks and maybe Claude is even a little better in some areas.
However, the note I see the most from Claude users is running out of usage.
Coding differences aside, this would be the biggest factor for me using one over the other. After several months on Codex's $20/mo. plan (and some pretty significant usage days), I have only come close to my usage limit once (never fully exceeded it).
That (at least to me) seems to be a much bigger deal than coding nuances.
In my experience, OpenAI gives you unreasonable amounts of compute for €20/month. I am subscribed to both and Claude's limits are so tiny compared to ChatGPT's that it often feels like a rip-off.
Claude also doesn't let you use a worse model after you reach your usage limits, which is a bit hard to swallow when you're paying for the service.
> the note I see the most from Claude users is running out of usage.
I suspect that tells us less about model capability/efficiency and more about each company's current need to paint a specific picture for investors re: revenue, operating costs, capital requirements, cash on hand, growth rate, retention, margins etc. And those needs can change at any moment.
Use whatever works best for your particular needs today, but expect the relative performance and value between leaders to shift frequently.
I only switched to using the terminal based agents in the last week. Prior to this I was pretty much only using it through Cursor and GH Copilot. The Anthropic models when used through GH Copilot were far superior to the codex ones and I didn't really get the hype of Codex. Using them through the CLI though, Codex is much better, IMO.
My guess is that it's potentially that and just momentum from developers who started using CC when it was far superior to Codex has allowed it to become so much more popular. Potentially, it's might be that, as it's more autonomous, it's better for true vibe-coding and it's more popular with the Twitter/LinkedIn wantrepreneur crew which meant it gets a lot of publicity which increases adoption quicker.
Out of curiosity, what do you feel are the key differences between cursor + models versus something like Claude Code/Codex?
Are you feeling the benefits of the switch? What prompted you to change?
I've been running cursor with my own workflows (where planning is definitely a key step) and it's been great. However, the feeling of missing out, coupled with the fact I am a paying ChatGPT customer, got me to try codex. It hasn't really clicked in what way this is better, as so far it really hasn't been.
I have this feeling that supposedly you can give these tools a bit more of a hands-off approach so maybe I just haven't really done that yet. Haven't fiddled with worktrees or anything else yet either.
Codex is great and I hit the usage once doing multiagent full 5 hour absolute degen session for the nornal workflow alongside never hit it and now x2 useage even and now with the planmode switch back and forth absolute great.
yea but i feel like we are over the hill on benchmaxxing, many times a model has beaten anthropic on a specific bench, but the 'feel' is that it is still not as good at coding
The variety of tasks they can do and will be asked to do is too wide and dissimilar, it will be very hard to have a transversal measurement, at most we will have area specific consensus that model X or Y is better, it is like saying one person is the best coder at everything, that does not exist.
The 'feel' of a single person is pretty meaningless, but when many users form a consensus over time after a model is released, it feels a lot more informative than a simple benchmark because it can shift over time as people individually discover the strong and weak points of what they're using and get better at it.
I don’t think this is even remotely true in practice.
I honestly I have no idea what benchmarks are benchmarking. I don’t write JavaScript or do anything remotely webdev related.
The idea that all models have very close performance across all domains is a moderately insane take.
At any given moment the best model for my actual projects and my actual work varies.
Quite honestly Opus 4.5 is proof that benchmarks are dumb. When Opus 4.5 released no one was particularly excited. It was better with some slightly large numbers but whatever. It took about a month before everyone realized “holy shit this is a step function improvement in usefulness”. Benchmarks being +15% better on SWE bench didn’t mean a damn thing.
Actually kind of excited for this. I've been using 5.2 for awhile now, and it's already pretty impressive if you set the context window to "high".
Something I have been experimenting with is AI-assisted proofs. Right now I've been playing with TLAPS to help write some more comprehensive correctness proofs for a thing I've been building, and 5.2 didn't seem quite up to it; I was able to figure out proofs on my own a bit better than it was, even when I would tell it to keep trying until it got it right.
I'm excited to see if 5.3 fairs a bit better; if I can get mechanized proofs working, then Fields Medal here I come!
> Using the develop web game skill and preselected, generic follow-up prompts like "fix the bug" or "improve the game", GPT‑5.3-Codex iterated on the games autonomously over millions of tokens.
I wish they would share the full conversation, token counts and more. I'd like to have a better sense of how they normalize these comparisons across version. Is this a 3-prompt 10m token game? a 30-prompt 100m token game? Are both models using similar prompts/token counts?
I vibe coded a small factorio web clone [1] that got pretty far using the models from last summer. I'd love to compare against this.
Thank you. There's a demo save to get the full feel of it quickly. There's also a 2D-ASCII and 3D render you can hotswap between. The 3D models are generated with Meshy. The entire game is 'AI slop'. I intentionally did no code reviews to see where that would get me. Some prompts were very specific but other prompts were just 'add a research of your choice'.
This was built using old versions of Codex, Gemini and Claude. I'll probably work on it more soon to try the latest models.
I've been listening to the insane 100x productivity gains you all are getting with AI and "this new crazy model is a real game changer" for a few years now, I think it's about time I asked:
Can you guys point me ton a single useful, majority LLM-written, preferably reliable, program that solves a non-trivial problem that hasn't been solved before a bunch of times in publicly available code?
I'm sure it's not perfect, and I'm sure there are lots of performance/productivity gains that can be made, but it's allowed us to connect our CDN based containers (which don't have root) across multiple regions, talking to each other on the same Wireguard network.
No product existed that I could find to do this (at least none I could find), and I could never build this (within the timeframe) without the help of AI.
In the 1930s, when electronic calculators were first introduced, there was a widespread belief that accounting as a career was finished. Instead, the opposite became true. Accounting as a profession grew, becoming far more analytical/strategic than it had been previously.
You are correct that these models primarily address problems that have already been solved. However, that has always been the case for the majority of technical challenges. Before LLMs, we would often spend days searching Stack Overflow to find and adapt the right solution.
Another way to look at this is through the lens of problem decomposition as well. If a complex problem is a collection of sub-problems, receiving immediate solutions for those components accelerates the path to the final result.
For example, I was recently struggling with a UI feature where I wanted cards to follow a fan-like arc. I couldn't quite get the implementation right until I gave it to Gemini. It didn't solve the entire problem for me, but it suggested an approach involving polar coordinates and sine/cosine values. I was able to take that foundational logic turn it into a feature I wanted.
Was it a 100x productivity gain? No. But it was easily a 2x gain, because it replaced hours of searching and waiting for a mental breakthrough with immediate direction.
There was also a relevant thread on Hacker News recently regarding "vibe coding":
The developer created a unique game using scroll behavior as the primary input. While the technical aspects of scroll events are certainly "solved" problems, the creative application was novel.
It doesn’t have to be, really. Even if it could replace 30% of documentation and SO scrounging, that’s pretty valuable. Especially since you can offload that and go take a coffee.
I think the 'better than googling' part is less about the final code and more about the friction.
For example, consider this game:
The game creates a target that's randomly generated on the screen and have a player at the middle of the screen that needs to hit the target. When a key is pressed, the player swings a rope attached to a metal ball in circles above it's head, at a certain rotational velocity. Upon key release, the player has to let go of the rope and the ball travels tangentially from the point of release. Each time you hit the target you score.
Now, I’m trying to calculate the tangential velocity of a projectile from a circular path, I could find the trig formulas on Stack Overflow. But with an LLM, I can describe the 'vibe' of the game mechanic and get the math scaffolded in seconds.
It's that shift from searching for syntax to architecting the logic that feels like the real win.
Why even come to this site if you're so anti-innovation?
Today with LLMs you can literally spend 5 minutes defining what you want to get, press send, go grab a coffee and come back to a working POC of something, in literally any programming language.
This is literally stuff of wonders and magic that redefines how we interface with computers and code. And the only thing you can think of is to ask if it can do something completely novel (that it's so hard to even quantity for humans that we don't have software patents mainly for that reason).
And the same model can also answer you if you ask it about maths, making you an itinerary or a recipe for lasagnas. C'mon now.
I don't think that the user you are responding to is anti-innovation, but rather points out that the usefulness of AI is oversold.
I'm using Copilot for Visual Studio at work. It is useful for me to speed some typing up using the auto-complete. On the other hand in agentic mode it fails to follow simple basic orders, and needs hand-holding to run. This might not be the most bleeding-edge setup, but the discrepancy between how it's sold and how much it actually helps for me is very real.
I think copilot is widely considered to be fairly rubbish, your description of agentic coding was also my experience prior to ~Q3 2025, but things have shifted meaningfully since then
I want AI that cures cancer and solves climate change. Instead we got AI that lets you plagiarize GPL code, does your homework for you, and roleplay your antisocial horny waifu fantasies.
Heh, I agree. There is a vast ocean of dev work that is just "upgrade criticalLib to v2.0" or adding support for a new field from the FE through to the BE.
I can name a few times where I worked on something that you could consider groundbreaking (for some values of groundbreaking), and even that was usually more the combination of small pieces of work or existing ideas.
As maybe a more poignant example- I used to do a lot of on-campus recruiting when I worked in HFT, and I think I disappointed a lot of people when I told them my day to day was pretty mundane and consisted of banging out Jiras, usually to support new exchanges, and/or securities we hadn't traded previously. 3% excitement, 97% unit tests and covering corner cases.
Well, it took opus 4.5 five messages to solve a trivial git problem for me. It hallucinated nonexistent flags three times. Hallucinating nonexistent flags is certainly a novel solution to my git ineptness.
Not to be outdone, chatgpt 5.2 thinking high only needed about 8 iterations to get a mostly-working ffmpeg conversion script for bash. It took another 5 messages to translate it to run in windows, on powershell (models escaping newlines on windows properly will be pretty nuch AGI, as far as I’m concerned).
Can you point me to a human written program an LLM cannot write? And no, just answering with a massively large codebase does not count because this issue is temporary.
> Can you point me to a human written program an LLM cannot write?
Sure:
"The resulting compiler has nearly reached the limits of Opus’s abilities. I tried (hard!) to fix several of the above limitations but wasn’t fully successful. New features and bugfixes frequently broke existing functionality.
As one particularly challenging example, Opus was unable to implement a 16-bit x86 code generator needed to boot into 16-bit real mode. While the compiler can output correct 16-bit x86 via the 66/67 opcode prefixes, the resulting compiled output is over 60kb, far exceeding the 32k code limit enforced by Linux. Instead, Claude simply cheats here and calls out to GCC for this phase (This is only the case for x86. For ARM or RISC-V, Claude’s compiler can compile completely by itself.)"[1]
I wish I could agree with you, but as a game dev, shader author, and occasional asm hacker, I still think AIs have demonstrated being perfectly capable of copying "those effects". It's been trained on them, of course.
You're not gonna one-shot RD2, but neither will a human. You can one-shot particles and shader passes though.
Why do you believe an LLM can't write these, just because they're 3D? If the assets are given (just as with a human game programmer, who has artists provide them the assets), then an LLM can write the code just the same.
What? People can easily get assets, thats not a even a problem in 2026. Roller coaster tycoon's assets were done by the programmer himself. If its so easy why haven't we seen actually complex pieces of software done in couple of weeks by LLM users?
Also try building any complex effects by prompting LLMs, you wont get any far, this is why all of the LLM coded websites look stupidly bland.
Not sure what you're confused about, I never said assets were hard to get, I just said that the LLM needs to be provided a folder of the assets for it to make use of them, it's not going to create them from scratch (at least not without great difficulty, because LLMs are capable of using and coding Three.js for example). I don't know the answer to your first question because I don't hang around in the 3D or game dev fields, I'm sure there are examples of vibe coded games however.
As to your second question, it is about prompting them correctly, for example [0]. Now I don't know about you but some of those sites especially after using the frontend skill look pretty good to me. If those look bland to you then I'm not really sure what you're expecting, keeping in mind that the example you showed with the graphics are not regular sites but more design oriented, and even still nothing stops LLMs from producing such sites.
you have shown me 0 examples, I showed actual examples to the given question. Your answers have just been "AI can also do this" but gave no actual proof.
The examples are in the video I linked, as I said, if you don't bother to watch it then I'm not sure what to tell you. As I said for games I don't know and won't presume to search up some random vibe coded game if I don't have personal experience with how LLMs handle games, but for web development, the sites I've made and seen made look pretty good.
No. AI does not work well enough, you still need a person to look on it and CODE. It probably never will, until AGI which probably also in my opinion will never come.
When 2 multi billion giants advertise same day, it is not competition but rather a sign of struggle and survival.
With all the power of the "best artificial intelligence" at your disposition, and a lot of capital also all the brilliant minds, THIS IS WHAT YOU COULD COME UP WITH?
What's funny is that most of this "progress" is new datasets + post-training shaping the model's behavior (instruction + preference tuning). There is no moat besides that.
"post-training shaping the models behavior" it seems from your wording that you find it not that dramatic. I rather find the fact that RL on novel environments providing steady improvements after base-model an incredibly bullish signal on future AI improvements. I also believe that the capability increase are transferring to other domains (or at least covers enough domains) that it represents a real rise in intelligence in the human sense (when measured in capabilities - not necessarily innate learning ability)
Many are saying codex is more interactive but ironically I think that very interactivity/determinism works best when using codex remotely as a cloud agent and in highly async cases. Conversely I find opus great locally, where I can ram messages into it to try to lever its autonomy best (and interrupt/clean up)
The behind the scenes on deciding when to release these models has got to be pretty insanely stressful if they're coming out within 30 minutes-ish of each other.
I wonder if their "5.3" was continuously being updated, with regenerated benchmarks with each improvement, and they just stayed ready to release it when claude released
This seems plausible. It would be shocking if these companies didn't have an automated testing suite which is recomputing these benchmarks on a regular basis, and uploading to a dashboard for supervision.
Given that they already pre-approved various language and marketing materials beforehand there's no real reason they couldn't just leave it lined up with a function call to go live once the key players make the call.
Could be, could also be situations where things are lined up to launch in the near future and then a mad dash happens upon receiving outside news of another launch happening.
I suppose coincidences happen too but that just seems too unlikely to believe honestly. Some sort of knowledge leakage does seem like the most likely reason.
The notes explicitly call out you may want to dial the effort setting back to medium to reduce latency/tokens (high being default, apparently there is a max setting too).
I would love to see a nutritional facts label on how many prompts / % of code / ratio of human involvement needed to use the models to develop their latest models for the various parts of their systems.
When using it in VSCode? The browser system running its own container seems like it would be the most demanding on their resources. The stand-alone client is Mac-only but I don't know if it makes a difference.
My goal is to do it within the usage I get from a $20 monthly plan.
There's hundreds of gameboy emulators available on Github they've been trained on. It's quite literally the simplest piece of emulation you could do. The fact that they couldn't do it before is an indictment of how shit they were, but a gameboy emulator should be a weekend project for anyone even ever so slightly qualified. Your benchmark was awful to begin with.
Or the same number of tokens in less time. Kinda feels like the CPU / modem wars of the 90s all over again - I remember those differences you felt going from a 386 -> 486 or from a 2400 -> 9600 baud modem.
We're in the 2400 baud era for coding agents and I for one look forward to the 56k era around the corner ;)
Specifically this paragraph is what I find hilarious.
> According to the report, the issue became apparent in OpenAI’s Codex, an AI code-generation tool. OpenAI staff reportedly attributed some of Codex’s performance limitations to Nvidia’s GPU-based hardware.
GPT-5.3-Codex dominates terminal coding with a roughly 12% lead (Terminal-Bench 2.0), while Opus 4.6 retains the edge in general computer use by 8% (OSWorld).
Anyone knows the difference between OSWorld vs OSWorld Verified?
OSWorld is the full 369-task benchmark. OSWorld Verified is a ~200-task subset where humans have confirmed the eval scripts reliably score success/failure — the full set has some noisy grading where correct actions can still get marked wrong.
Scores on Verified tend to run higher, so they're not directly comparable.
i like the opus 4.6 announcement a lot more, concise and to the point. for the 5.3 codex, it's a long post, but still, the most important info, the context window, is nowhere to be found.
thus, I'm keeping using opus.
Doesn't feel like a useful data point without more context. For some hard bugs I'd be thrilled to wait 30 minutes for a fix, for a trivial CSS fix not so much. I've spent weeks+ of my career fix single bugs. Context is everything.
Sure, but I've never experienced a 20 minute wait with CC before. It was an architectural question but it would have taken a couple minutes with a definitive answer on 4.5.
I never really used Codex (found it to slow) just 5.2, which I going to be an excellent model for my work. This looks like another step up.
This week, I'm all local though, playing with opencode and running qwen3 coder next on my little spark machine. With the way these local models are progressing, I might move all my llm work locally.
It's so difficult to compare these models because they're not running the same set of evals. I think literally the only eval variant that was reported for both Opus 4.6 and GPT-5.3-Codex is Terminal-Bench 2.0, with Opus 4.6 at 65.4% and GPT-5.3-Codex at 77.3%. None of the other evals were identical, so the numbers for them are not comparable.
Isn't the best eval the one you build yourself, for your own use cases and value production?
I encourage people to try. You can even timebox it and come up with some simple things that might look initially insufficient but that discomfort is actually a sign that there's something there. Very similar to moving from not having unit/integration tests for design or regression and starting to have them.
I also wasn't that familiar with it, but the Opus 4.6 announcement leaned pretty heavily on the TerminalBench 2.0 score to quantify how much of an improvement it was for coding, so it looks pretty bad for Anthropic that OpenAI beat them on that specific benchmark so soundly.
Looking at the Opus model card I see that they also have by far the highest score for a single model on ARC-AGI-2. I wonder why they didn't advertise that.
No way! Must be a coinkydink, no way OpenAI knew ahead of time that Anthropic was gonna put a focus on that specific useless benchmark as opposed to all the other useless benchmarks!?
I find it very, very interesting how they demoed visuals in the form of the “soft SaaS” website and mentioned how it can do user research. Codex has usually lagged behind Claude and Gemini when it comes to UX, so I’m curious to see if 5.3 will take the lead in real world use. Perhaps it’ll be available in Figma Make now?
May I at least understand what it has "written". AI help is good but don't replace real programmers completely. I'm enough copy pasting code i don't understand. What if one day AI will fall down and there will be no real programmers to write the software. AI for help is good but I don't want AI to write whole files into my project. Then something may broke and I won't know what's broken. I've experienced it many times already. Told the AI to write something for me. The code was not working at all. It was compiling normally but the program was bugged. Or when I was making some bigger project with ChatGPT only, it was mostly working but after a longer time when I was promting more and more things, everything got broken.
I've tried that too but it was almost the same, chatgpt kept forgetting many things about the code and project structure.
In summary AI can get problematic for me and i get with troubles with it.
This is like one of the reasons why I still prefer traditional text editor for writing code like Vim over a "software on steroids" like VS Code and things like that...
> What if one day AI will fall down and there will be no real programmers to write the software.
What if you want to write something very complex now that most people don't understand? You keep offering more money until someone takes the time to learn it and accomplish it, or you give up.
I mean, there are still people that hammer out horseshoes over a hot fire. You can get anything you're willing to pay money for.
I'm afraid of all of the modern world especially in technology, I guess if now I would "come back" to all of modern and new things: the commercialized world, AI, corporations, etc...my head would explode. I mean I can't imagine living in such world. I am not sure if everything would be alright eith myself in all this everything,This is just too much...
OpenAI has a whole history of trying to scoop other providers. This was a whole thing for Google launches, where OpenAI regularly launched something just before Google to grab the media attention.
GPT-4o vs. Google I/O (May 2024): OpenAI scheduled its "Spring Update" exactly 24 hours before Google’s biggest event of the year, Google I/O. They launched GPT-4o voice mode.
Sora vs. Gemini 1.5 Pro (Feb 2024): Just two hours after Google announced its breakthrough Gemini 1.5 Pro model, Sam Altman tweeted the reveal of Sora (text-to-video).
ChatGPT Enterprise vs. Google Cloud Next (Aug 2023): As Google began its major conference focused on selling AI to businesses, OpenAI announced ChatGPT Enterprise.
Having used codex a fair bit I find it really struggles with … almost anything. However using the equivalent chat gpt model is fantastic. I guess it’s a matter of focus and being provided with a smaller set of code to tackle.
I think this announcement says a lot about OpenAI and their relationship to partners like Microsoft and NVIDIA, not to mention the attitude of their leadership team.
On Microsoft Foundry I can see the new Codex 4.6 model right now, but GPT-5.3 is nowhere to be seen.
I have a pre-paid account directly with OpenAI that has credits, but if I use that key with the Codex CLI, it can't access 5.3 either.
The press release very prominently includes this quote: "GPT‑5.3-Codex was co-designed for, trained with, and served on NVIDIA GB200 NVL72 systems. We are grateful to NVIDIA for their partnership."
Sounds like OpenAI's ties with their vendors are fraying while at the same time they're struggling to execute on the basics like "make our own models available to our own coding agents", let alone via third-party portals like Microsoft Foundry.
OpenAI models in general, yes - `opencode auth login`, select OpenAI, then ChatGPT Pro/Plus. I just checked and 5.3-codex isn't available in opencode yet, but I assume it will be soon.
You can also use via Opencode Zen, Github Copilot, or probably any number of other model providers that Opencode integrates with.
Not sure why everyone stays focused on getting it from Anthropic or OpenAI directly when there are so many places to get access to these models and many others for the same or less money.
I've tried opus 4.5 in opencode via the GitHub Copilot API, mostly to see if it works all. I don't think that broke any terms of service? But also I haven't checked how much more expensive I made it for myself over just calling them directly.
I am on a max subscription for Claude, and hate the fact that OpenAI have not figured out that $20 => $200 is a big jump. Good luck to them. In terms of model, just last night, Codex 5.2 solved a problem for me which other models were going round and round. Almost same instructions. That said, I still plan to be on $100 Claude (overall value across many tasks, ability to create docs, co-work), and may bump up OpenAI subscription to the next tier should they decide to introduce one. Not going to $200 even with 5.3, unless my company pays for it.
I'm coding about 6-9h per day with Codex CLI on the $20 Plus sub, occasionally switching to extra-high reasoning and feeding it massive contexts, all tools enabled, sometimes 2-3 terminal sessions running in parallel and I've never hit limits... I operate on small-ish codebases but even so I try to work in the most local scope possible with AGENTS.md at the sub-directory levels.
Are you really hitting limits, or are you turned off by the fact you think you will?
You are correct :-) I am turned off by the fact that I will hit the limit if I used more. But you gave me confidence. I guess $20 can go a long way. I think only once in the last 3 months I got rate limited in Codex.
You should look into Kilo Pass by Kilo Code (https://kilo.ai/features/kilo-pass). It's basically a fixed subscription and your credits roll over each month, and you get free extra credits too which are used up first before paid credits. It's similar to paying for Cursor except the credits roll over which is why I'm contemplating moving to it, because I don't want to be locked into any one LLM provider the way Claude Code or Codex make you become.
Yeah, Claude Code is what everyone is talking about these days and since OpenAI has always been the spending driver being 2nd or 3rd fiddle just isn't acceptable if they're gonna justify it.
It's where the revenue is, but it isn't going to be where the profit is. Developers will easily use absurdly large amounts of compute, costing the AI provider a lot more than they receive in revenue.
I agree, I was confused about where 5.3 non Codex was. 5.2-Codex disappointed me enough that I won't be giving 5.3 Codex a try, but I'm looking forward to trying 5.3 non Codex with Pi.
GPT-5.x in general are very disappointing, the only good chat model was GPT-5 in the first week before they made "the personality warmer" and Codex in general was always kinda meh.
I know we just got a reset and a 2× bump with the native app release, but shipping 5.3 with no reset feels mismatched. If I’d known this was coming, I wouldn’t have used up the quota on the previous model.
Can you elaborate what you mean - OAI structured outputs means JSON schema doesn't it? So are you just saying they both support JSON schema but Anthropic has a limitation?
OpenAI, in addition to JSON schema, supports "context-free grammar"[0], i.e. regex and lark. Anthropic also supports JSON schema since a few weeks ago, but they don't support specifying the length of JSON array, so you still have to worry about the model producing invalid output.
One thing that pisses me off is this widespread misunderstanding that you can just fall back to function calling (Anthropic's function calling accepts JSON schema for arguments), and that it's the same as structured outputs. It is not. They just dump the JSON schema into the context without doing the actual structured outputs. Vercel's AI SDK does that and it pisses me off because doing that only confuses the model and prefilling works much better.
Whats interesting to me is that these gpt-5.3 and opus-4.6 are diverging philosophically and really in the same way that actual engineers and orgs have diverged philosophically
With Codex (5.3), the framing is an interactive collaborator: you steer it mid-execution, stay in the loop, course-correct as it works.
With Opus 4.6, the emphasis is the opposite: a more autonomous, agentic, thoughtful system that plans deeply, runs longer, and asks less of the human.
that feels like a reflection of a real split in how people think llm-based coding should work...
some want tight human-in-the-loop control and others want to delegate whole chunks of work and review the result
Interested to see if we eventually see models optimize for those two philosophies and 3rd, 4th, 5th philosophies that will emerge in the coming years.
Maybe it will be less about benchmarks and more about different ideas of what working-with-ai means
> With Codex (5.3), the framing is an interactive collaborator: you steer it mid-execution, stay in the loop, course-correct as it works.
> With Opus 4.6, the emphasis is the opposite: a more autonomous, agentic, thoughtful system that plans deeply, runs longer, and asks less of the human.
Ain't the UX is the exact opposite? Codex thinks much longer before gives you back the answer.
Yes, you’re right for 4.5 and 5.2. Hence they’re focusing on improving the opposite thing and thus are actually converging.
I've also had the exact opposite experience with tone. Claude Code wants to build with me, and Codex wants to go off on its own for a while before returning with opinions.
Its likely that both are steering towards the middle from their current relative extremes and converging to nearly the same place.
also my experience in using these two models. they are trying to recover from oversteer perhaps.
This is most likely an inference serving problem in terms of capacity and latency given that Opus X and the latest GPT models available in the API have always responded quickly and slowly, respectively
Codex now lets you tell the LLM tgings in the middle of its thinking without interrupting it, so you can read the thinking traces and tell it to change course if it's going off track.
That just seems like a UI difference. I've always interrupted claude code added a comment and it's continued without much issue. Otherwise if you just type the message is queued for next. There's no real reason to prefer one over the other except it sounds like codex can't queue messages?
I'm personally 100% convinced (assuming prices stay reasonable) that the Codex approach is here to stay.
Having a human in the loop eliminates all the problems that LLMs have and continously reviewing small'ish chunks of code works really well from my experience.
It saves so much time having Codex do all the plumbing so you can focus on the actual "core" part of a feature.
LLMs still (and I doubt that changes) can't think and generalize. If I tell Codex to implement 3 features he won't stop and find a general solution that unifies them unless explicitly told to. This makes it kinda pointless for the "full autonomy" approach since effecitly code quality and abstractions completely go down the drain over time. That's fine if it's just prototyping or "throwaway" scripts but for bigger codebases where longevity matters it's a dealbreaker.
I'm personally 100% convinced of the opposite, that it's a waste of time to steer them. we know now that agentic loops can converge given the proper framing and self-reflectiveness tools.
Converge towards what though... I think the level of testing/verification you need to have an LLM output a non-trivial feature (e.g. Paxos/anything with concurrency, business logic that isn't just "fetch value from spreadsheet, add to another number and save to the database") is pretty high.
in the new world, engineers have to actually be good at capturing and interpreting requirements
Does the AI agent know what your company is doing right now, what every coworker is working on, how they are doing it, and how your boss will change priorities next month without being told?
If it really knows better, then fire everyone and let the agent take charge. lol
No, but Codex wouldn’t have asked you those questions either
I think it's the opposite. Especially considering Codex started out as a web app that offers very little interactivity: you are supposed to drop a request and let it run automatously in a containerized environment; you can then follow up on it via chat --- no interactive code editing.
Fair I agree that was true of early codex and my perception too.. but today there are two announcements that came out and thats what im referring to.
specifically, the GPT-5.3 post explicitly leans into "interactive collaborator" langauge and steering mid execution
OpenAI post: "Much like a colleague, you can steer and interact with GPT-5.3-Codex while it’s working, without losing context."
OpenAI post: "Instead of waiting for a final output, you can interact in real time—ask questions, discuss approaches, and steer toward the solution"
Claude post: "Claude Opus 4.6 is designed for longer-running, agentic work — planning complex tasks more carefully and executing them with less back-and-forth from the user."
I think there is another philosophy where the agent is domain specific. Not that we have to invent an entirely new universe for every product or business, but that there is a small amount of semi-customization involved to achieve an ideal agent.
I would much rather work with things like the Chat Completion API than any frameworks that compose over it. I want total control over how tool calling and error handling works. I've got concerns specific to my business/product/customer that couldn't possibly have been considered as part of these frameworks.
Whether or not a human needs to be tightly looped in could vary wildly depending on the specific part of the business you are dealing with. Having a purpose-built agent that understands where additional verification needs to occur (and not occur) can give you the best of both worlds.
Good breakdown.
I usually want the codex approach for code/product "shaping" iteratively with the ai.
Once things are shaped and common "scaling patterns" are well established, then for things like adding a front end (which is constantly changing, more views) then letting the autonomous approach run wild can *sometimes* be useful.
I have found that codex is better at remembering when I ask to not get carried away...whereas claude requires constant reminders.
This kind of sounds like both of them stepping into the other’s turf, to simplify a bit.
I haven’t used Codex but use Claude Code, and the way people (before today) described Codex to me was like how you’re describing Opus 4.6
So it sounds like they’re converging toward “both these approaches are useful at different times” potentially? And neither want people who prefer one way of working to be locked to the other’s model.
> With Opus 4.6, the emphasis is the opposite: a more autonomous, agentic, thoughtful system that plans deeply, runs longer, and asks less of the human.
This feels wrong, I can't comment on Codex, but Claude will prompt you and ask you before changing files, even when I run it in dangerous mode on Zed, I can still review all the diffs and undo them, or you know, tell it what to change. If you're worried about it making too many decisions, you can pre-prompt Claude Code (via .claude/instructions.md) and instruct it to always ask follow up questions regarding architectural decisions.
Sometimes I go out of my way to tell Claude DO NOT ASK ME FOR FOLLOW UPS JUST DO THE THING.
yeah I'm mostly just talking about how they're framing it: "Claude Opus 4.6 is designed for longer-running, agentic work — planning complex tasks more carefully and executing them with less back-and-forth from the user"
I guess its also quite interesting that how they are framing these projects are opposite from how people currently perceive them and I guess that may be a conscious choice...
I get what you mean now, I like that to be fair, sometimes I want Claude to tell me some architectural options, so I ask it so I can think about what my options are, sometimes I rethink my problem if I like Claudes conclusion.
> With Codex (5.3), the framing is an interactive collaborator: you steer it mid-execution, stay in the loop, course-correct as it works.
This is true, but I find that Codex thinks more than Opus. That's why 5.2 Codex was more reliable than Opus 4.5
How can they be diverging, LLMs are built on similar foundations aka the Transformer architecture. Do you mean the training method (RLHF) is diverging?
I'm not OP but I suspect they are meaning the products / tooling / company direction, not necessarily the underlying LLM architecture.
Funny cause the situation was totally flipped last iteration.
Just because you can inject steering doesn't mean they stered away from long running...
Theres hundreds of people who upload Codex 5.2 running for hours unattended and coming back with full commits
Boing vs airbus philosophy
I read this exact comment with I would say completely the same words several times in X and I would bet my money it's LLM generated by someone who has not even tried both the tools. This AI slop even in the site like this without direct monetisation implications from fake engagement is making me sick...
I am definitely using Opus as an interactive collaborator that I steer mid-execution, stay in the loop and course correct as it works.
I mean Opus asks a lot if he should run things, and each time you can tell it to change. And if that's not enough you can always press esc to interrupt.
I think Anthropic rushed out the release before 10am this morning to avoid having to put in comparisons to GPT-5.3-codex!
The new Opus 4.6 scores 65.4 on Terminal-Bench 2.0, up from 64.7 from GPT-5.2-codex.
GPT-5.3-codex scores 77.3.
I do not trust the AI benchmarks much, they often do not line up with my experience.
That said ... I do think Codex 5.2 was the best coding model for more complex tasks, albeit quite slow.
So very much looking forward to trying out 5.3.
Just some anecdata++ here but I found 5.2 to be really good at code review. So I can have something crunched by cheaper models, reviewed async by codex and then re-prompt with the findings from the review. It finds good things, doesn't flag nits (if prompted not to) and the overall flow is worth it for me. Speed loss doesn't impact this flow that much.
Personally, I have Claude do the coding. Then 5.2-high do the reviewing.
Then I pass the review back to Claude Opus to implement it.
Just curious is this a manual process or you guys have automated these steps?
I have a `codex-review` skill with a shell script that uses the Codex CLI with a prompt. It tells Claude to use Codex as a review partner and to push back if it disagrees. They will go through 3 or 4 back-and-forth iterations some times before they find consensus. It's not perfect, but it does help because Claude will point out the things Codex found and give it credit.
Mind sharing the skill/prompt?
zen-mcp (now called pal-mcp I think) and then claude code can actually just pass things to gemini (or any other model)
Sometimes, depends on how big of a task. I just find 5.2 so slow.
I have Opus 4.5 do everything then review it with Gemini 3.
I don’t use OpenAI too much, but I follow a similar work flow. Use Opus for design/architecture work. Move it to Sonnet for implementation and build out. Then finally over to Gemini for review, QC and standards check. There is an absolute gain in using different models. Each has their own style and way of solving the problem just like a human team. It’s kind of awesome and crazy and a bit scary all at once.
How do you orchestrate this workflow? Do you define different skills that all use different models, or something else?
5.2 Codex became my default coding model. It “feels” smarter than Opus 4.5.
I use 5.2 Codex for the entire task, then ask Opus 4.5 at the end to double check the work. It's nice to have another frontier model's opinion and ask it to spot any potential issues.
Looking forward to trying 5.3.
Opus 4.5 is more creative and better at making UIs
Yeah, these benchmarks are bogus.
Every new model overfits to the latest overhyped benchmark.
Someone should take this to a logical extreme and train a tiny model that scores better on a specific benchmark.
All shared machine learning benchmarks are a little bit bogus, for a really “machine learning 101” reason: your test set only yields an unbiased performance metric if you agree to only use it once. But that just isn’t a realistic way to use a shared benchmark. Using them repeatedly is kind of the whole point.
But even an imperfect yardstick is better than no yardstick at all. You’ve just got to remember to maintain a healthy level of skepticism is all.
Is an imperfect yardstick better than no yardstick? It reminds me of documentation — the only thing worse than no documentation is wrong documentation.
> Yeah, these benchmarks are bogus.
It's not just over-fitting to leading benchmarks, there's also too many degrees of freedom in how a model is tested (harness, etc). Until there's standardized documentation enabling independent replication, it's all just benchmarketing .
For the current state of AI, the harness is unfortunately part of the secret sauce.
This has been done: https://arxiv.org/abs/2510.04871v1
Opus 4.5 still worked better for most of my work, which is generally "weird stuff". A lot of my programming involves concepts that are a bit brain-melting for LLMs, because multiple "99% of the time, assumption X is correct" are reversed for my project. I think Opus does better at not falling into those traps. Excited to try out 5.3
what do you do?
Another day, another hn thread of "this model changes everything" followed immediately by a reply stating "actually I have the literal opposite experience and find competitor's model is the best" repeated until it's time to start the next day's thread.
What amazes me the most is the speed at which things are advancing. Go back a year or even a year before that and all these incremental improvements have compounded. Things that used to require real effort to consistently solve, either with RAGs, context/prompt engineering, have become… trivial. I totally agree with your point that each step along the way doesn’t necessarily change that much. But in the aggregate it’s sort of insane how fast everything is moving.
The denial of this overall trend on here and in other internet spaces is starting to really bother me. People need to have sober conversations about the speed of this increase and what kind of effects it's going to have on the world.
I use Claude Code every day, and I'm not certain I could tell the difference between Opus 4.5 and Opus 4.0 if you gave me a blind test
And of course the benchmarks are from the school of "It's better to have a bad metric than no metric", so there really isn't any way to falsify anyone's opinions...
This pretty accurately summarizes all the long discussions about AI models on HN.
Hourly occurrence on /r/codex. Model astrology is about the vibes.
they tested it at xhigh reasoning though, which is probably double the cost of Anthropic's model.
Cost to Run Artificial Analysis Intelligence Index:
GPT-5.2 Codex (xhigh): $3244
Claude Opus 4.5-reasoning: $1485
(and probably similar values for the newer models?)
With $20 gpt plan you can use xhigh no problem. With $20 Claude plan you reach the 5h limit with a single feature.
Ha, Claude Code on a pro plan often can't complete a single message before hitting the 5h limit. Not hit it once so far on Codex.
This, so frustrating. But CC is so much faster too.
A provider's API costs seemingly do not reflect each respective SOTA provider's subscription usage allowances.
Impressive jump for GPT-5.3-codex and crazy to see two top coding models come out on the same day...
Insane! I think this has to be the shortest-lived SOTA for any model so far. Competition is amazing.
In my personal experience the GPT models have always been significantly better than the Claude models for agentic coding, I’m baffled why people think Claude has the edge on programming.
I think for many/most programmers = 'speed + output' and webdev == "great coding".
Not throwing shade anyone's way. I actually do prefer Claude for webdev (even if it does cringe things like generate custom CSS on every page) -- because I hate webdev and Claude designs are always better looking.
But the meat of my code is backend and "hard" and for that Codex is always better, not even a competition. In that domain, I want accuracy and not speed.
Solution, use both as needed!
> I actually do prefer Claude for webdev
Ah and let me guess all your frontends look like cookie cutter versions of this: https://openclaw.dog/
Yes and I love it.
> Solution, use both as needed!
This is the way. People are unfortunately starting to divide themselves into camps on this — it’s human nature we’re tribal - but we should try to avoid turning this into a Yankees Redsox.
Both companies are producing incredible models and I’m glad they have strengths because if you use them both where appropriate it means you have more coverage for important work.
GPT 5.2 codex plans well but fucks off a lot, goes in circles (more than opus 4.5) and really just lacks the breadth of integrated knowledge that makes opus feel so powerful.
Opus is the first model I can trust to just do things, and do them right, at least small things. For larger/more complex things I have to keep either model on extremely short leashes. But the difference is enough that I canceled my GPT Pro sub so I could switch to Claude. Maybe 5.3 will change things, but I also cannot continue to ethically support Sam Altman's business.
I always use 5.2-Codex-High or 5.2-Codex-Extra High (in Cursor). The regular version is probably too dumb.
Didn't make a difference for me. Though I will say, so far 4.6 is really pissing me off and I might downgrade back to 4.5. It just refuses to listen to what I say, the steering is awful.
How many people are building the same thing multiple times to compare model performance? I'm much more interested in getting the thing I'm building getting built, than than comparing AIs to each other.
Did you look at the ARC AGI 2? Codex might be overfit for terminal bench
ARC AGI 2 has a training set that model providers can choose to train on, so really wouldn't recommend using it as a general measure of coding ability.
A key aspect of ARC AGI is to remain highly resistant to training on test problems which is essential for ARC AGI's purpose of evaluating fluid intelligence and adaptability in solving novel problems. They do release public test sets but hold back private sets. The whole idea is being a test where training on public test sets doesn't materially help.
The only valid ARC AGI results are from tests done by the ARC AGI non-profit using an unreleased private set. I believe lab-conducted ARC AGI tests must be on public sets and taken on a 'scout's honor' basis that the lab self-administered the test correctly, didn't cheat or accidentally have public ARC AGI test data slip into their training data. IIRC, some time ago there was an issue when OpenAI published ARC AGI 1 test results on a new model's release which the ARC AGI non-profit was unable to replicate on a private set some weeks later (to be fair, I don't know if these issues were resolved). Edit to Add: Summary of what happened: https://grok.com/share/c2hhcmQtMw_66c34055-740f-43a3-a63c-4b...
I have no expertise to verify how training-resistant ARC AGI is in practice but I've read a couple of their papers and was impressed by how deeply they're thinking through these challenges. They're clearly trying to be a unique test which evaluates aspects of 'human-like' intelligence other tests don't. It's also not a specific coding test and I don't know how directly ARC AGI scores map to coding ability.
More fundamentally, ARC is for abstract reasoning. Moving blocks around on a grid. While in theory there is some overlap with SWE tasks, what I really care about is competence on the specific task I will ask it to do. That requires a lot of domain knowledge.
As an analogy, Terence Tao may be one of the smartest people alive now, but IQ alone isn’t enough to do a job with no domain-specific training.
Opus was quite useless today. Created lots of globals, statics, forward declarations, hidden implementations in cpp files with no testable interface, erasing types, casting void pointers, I had to fix quite a lot and decouple the entangled mess.
Hopefully performance will pick up after the rollout.
Did you give it any architecture guidance? An architecture skill that it can load to make sure it lays out things according to your taste?
Yes, it has a very tight CLAUDE.md which it used to follow. Feels like this happens a couple of times a month.
,,GPT‑5.3-Codex is the first model we classify as High capability for cybersecurity-related tasks under our Preparedness Framework , and the first we’ve directly trained to identify software vulnerabilities. While we don’t have definitive evidence it can automate cyber attacks end-to-end, we’re taking a precautionary approach and deploying our most comprehensive cybersecurity safety stack to date. Our mitigations include safety training, automated monitoring, trusted access for advanced capabilities, and enforcement pipelines including threat intelligence.''
While I love Codex and believe it's amazing tool, I believe their preparedness framework is out of date. As it is more and more capable of vibe coding complex apps, it's getting clear that the main security issues will come up by having more and more security critical software vibe coded.
It's great to look at systems written by humans and how well Codex can be used against software written by humans, but it's getting more important to measure the opposite: how well humans (or their own software) are able to infiltrate complex systems written mostly by Codex, and get better on that scale.
In simpler terms: Codex should write secure software by default.
Is "high-capability" a stronger or weaker claim than "team of phd-level experts"?
https://www.nbcnews.com/tech/tech-news/openai-releases-chatg...
That’s just classical OpenAI trying to make us believe they’re closing on AGI… Like all « so called » research from them and Anthropic about safety alignment and that their tech is so incredibly powerful that guardrails should be put on them.
I heard the other day that every time someone claps another vibe coded project embeds the api keys in the webpage.
I wonder if this will continue to be the case.
>Our mitigations include safety training, automated monitoring, trusted access for advanced capabilities, and enforcement pipelines including threat intelligence.
"We added some more ACLs and updated our regex"
Please no, I don’t need my quick prototypes hardened against every perceivable threat.
Something that caught my eye from the announcement:
> GPT‑5.3‑Codex is our first model that was instrumental in creating itself. The Codex team used early versions to debug its own training
I'm happy to see the Codex team moving to this kind of dogfooding. I think this was critical for Claude Code to achieve its momentum.
Sounds like the researchers behind https://ai-2027.com/ haven't been too far off so far.
We'll see. The first two things that they said would move from "emerging tech" to "currently exists" by April 2026 are:
- "Someone you know has an AI boyfriend"
- "Generalist agent AIs that can function as a personal secretary"
I'd be curious how many people know someone that is sincerely in a relationship with an AI.
And also I'd love to know anyone that has honestly replaced their human assistant / secretary with an AI agent. I have an assistant, they're much more valuable beyond rote input-output tasks... Also I encourage my assistant to use LLMs when they can be useful like for supplementing research tasks.
Fundamentally though, I just don't think any AI agents I've seen can legitimately function as a personal secretary.
Also they said by April 2026:
> 22,000 Reliable Agent copies thinking at 13x human speed
And when moving from "Dec 2025" to "Apr 2026" they switch "Unreliable Agent" to "Reliable Agent". So again, we'll see. I'm very doubtful given the whole OpenClaw mess. Nothing about that says "two months away from reliable".
> Someone you know has an AI boyfriend
MyBoyfriendIsAI is a thing
> Generalist agent AIs that can function as a personal secretary
Isn't that what MoltBot/OpenClaw is all about?
So far these look like successful predictions.
Moltbot is an attempt to do that. Would you hire it as a personal secretary and entrust all your personal data to it?
Only people who haven't had a secretary would think it's a personal secretary.
Like, it can't even answer the phone.
I think they immediately corrected their median timelines for takeoff to 2028 upon releasing the article (I believe there was a math mistake or something initially), so all those dates can probably be bumped back a few months. Regardless, the trend seems fairly on track.
I don't think generative AI is even close to making model development 50% faster
More importantly, this is the early steps of a model self improving itself.
Do we still think we'll have soft take off?
> Do we still think we'll have soft take off?
There's still no evidence we'll have any take off. At least in the "Foom!" sense of LLMs independently improving themselves iteratively to substantial new levels being reliably sustained over many generations.
To be clear, I think LLMs are valuable and will continue to significantly improve. But self-sustaining runaway positive feedback loops delivering exponential improvements resulting in leaps of tangible, real-world utility is a substantially different hypothesis. All the impressive and rapid achievements in LLMs to date can still be true while major elements required for Foom-ish exponential take-off are still missing.
Yes, but also you'll never have any early evidence of the Foom until the Foom itself happens.
If only General Relativity had such an ironclad defense of being as unfalsifiable as Foom Hypothesis is. We could’ve avoided all of the quantum physics nonsense.
Exponential growth may look like a very slow increase at first, but it's still exponential growth.
Sigmoids may look like exponential growth at first, until they saturate. Early growth alone cannot distinguish between them.
If it's exponential growth. It may just as well be some slow growth and continue to be so.
I'm only saying no to keep optimistic tbh
It feels crazy to just say we might see a fundamental shift in 5 years.
But the current addition to compute and research etc. def goes in this direction I think.
I think the limiting factor is capital, not code. And I doubt GPTX is anymore competent at raising funds than the other, fleshy, snake oilers...
making the specifications is still hard, and checking how well results match against specifications is still hard.
i dont think the model will figure that out on its own, because the human in the loop is the verification method for saying if its doing better or not, and more importantly, defining better
This has already been going on for years. It's just that they were using GPT 4.5 to work on GPT 5. All this announcement mean is that they're confident enough in early GPT 5.3 model output to further refine GPT 5.3 based on initial 5.3. But yes, takeoff will still happen because of this recursive self improvement works, it's just that we're already past the inception point.
I guess humans were involved in all that, so how is that anything but tool use?
I can't tell if this is a serious conversation anymore.
“Best start believing in science fiction stories. You're in one.”
https://x.com/TheZvi/status/2017310187309113781
I remember when AI labs coordinated so they didn't push major announcements on the same day to avoid cannibalizing each other. Now we have AI labs pushing major announcements within 30 minutes.
The labs have fully embraced the cutthroat competition, the arms race has fully shed the civilized facade of beneficient mutual cooperation.
Dirty tricks and underhanded tactics will happen - I think Demis isn't savvy in this domain, but might end up stomping out the competition on pure performance.
Elon, Sam, and Dario know how to fight ugly and do the nasty political boardroom crap. 26 is gonna be a very dramatic year, lots of cinematic potential for the eventual AI biopics.
>civilized facade of mutual cooperation
>Dirty tricks and underhanded tactics
As long the tactics are legal ( i.e. not corporate espionage, bribes etc), the no holds barred full free market competition is the best thing for the market and the consumers.
> As long the tactics are legal ( i.e. not corporate espionage, bribes etc), the no holds barred full free market competition is the best thing for the market and the consumers.
The implicit assumption here is that we have constructed our laws so skillfully that the only path to win a free market competition is by producing a better product, or that all efforts will be spent doing so. This is never the case. It should be self-evident from this that there is a more productive way for companies to compete and our laws are not sufficient to create the conditions.
Yes, but not cutthroat competition that implies unsustainable, detrimental competition that kills off the industry.
The consumers are getting huge wins.
Model costs continue to collapse while capability improves.
Competition is fantastic.
> The consumers are getting huge wins.
However, the investors currently subsidizing those wins to below cost may be getting huge losses.
in the short term maybe, in the long term it depends how many winners you have. If only two, the market will be a duopoly. Customers will get better AI but will have zero power over the way the AI is produced or consumed (i.e. cO2 emission, ethics, etc will be burnt)
Sure, it can be beneficial. But don't forget that externalities are a thing.
They're also coordinating around Chinese New Year to compete with new releases of the major open/local models.
Year of the Pelican!
simonw?
This goes way back. When OpenAI launched GPT-4 in 2023, both Anthropic and Google lined up counter launches (Claude and Magic Wand) right before OpenAI's standard 10am launch time.
The thrill of competition
Wouldn't that be illegal ? i.e. cartel to collude like that ?
A sign of the inevitible implosion !
I wish they’d just stop pretending to care about safety, other than a few researchers at the top they care about safety only as long as they aren’t losing ground to the competition. Game theory guarantees the AI labs will do what it takes to ensure survival. Only regulation can enforce the limits, self policing won’t work when money is involved.
The last thing I would want is for excessively neurotic bureaucrats to interfere with all the mind-blowing progress we've had in the last couple of years with LLM technology.
As long as China continues to blitz forward, regulation is a direct path to losing.
Define "losing."
Europe is prematurely regarded as having lost the AI race. And yet a large portion of Europe live higher quality lives compared to their American counterparts, live longer, and don't have to worry about an elected orange unleashing brutality on them.
If the world is built on AI infrastructure (models, compute, etc.) that is controlled by the CCP then the west has effectively lost.
This may lead to better life outcomes, but if the west doesn't control the whole stack then they have lost their sovereignty.
This is already playing out today as Europe is dependent on the US for critical tech infrastructure (cloud, mail, messaging, social media, AI, etc). There's no home grown European alternatives because Europe has failed to create an economic environment to assure its technical sovereignty.
Europe has already lost the tech race - their cloud systems that their entire welfare states rely upon are all hosted on servers hosted by American private companies, which can turn them off with a flick of a switch if and when needed.
When the welfare state, enabled by technology, falls apart, it won't take long for European society to fall apart. Except France maybe.
You mean all paths are direct paths to losing.
I've always been fascinated to see significantly more people talking about using Claude than I see people talking about Codex.
I know that's anecdotal, but it just seems Claude is often the default.
I'm sure there are key differences in how they handle coding tasks and maybe Claude is even a little better in some areas.
However, the note I see the most from Claude users is running out of usage.
Coding differences aside, this would be the biggest factor for me using one over the other. After several months on Codex's $20/mo. plan (and some pretty significant usage days), I have only come close to my usage limit once (never fully exceeded it).
That (at least to me) seems to be a much bigger deal than coding nuances.
In my experience, OpenAI gives you unreasonable amounts of compute for €20/month. I am subscribed to both and Claude's limits are so tiny compared to ChatGPT's that it often feels like a rip-off.
Claude also doesn't let you use a worse model after you reach your usage limits, which is a bit hard to swallow when you're paying for the service.
> the note I see the most from Claude users is running out of usage.
I suspect that tells us less about model capability/efficiency and more about each company's current need to paint a specific picture for investors re: revenue, operating costs, capital requirements, cash on hand, growth rate, retention, margins etc. And those needs can change at any moment.
Use whatever works best for your particular needs today, but expect the relative performance and value between leaders to shift frequently.
I only switched to using the terminal based agents in the last week. Prior to this I was pretty much only using it through Cursor and GH Copilot. The Anthropic models when used through GH Copilot were far superior to the codex ones and I didn't really get the hype of Codex. Using them through the CLI though, Codex is much better, IMO.
My guess is that it's potentially that and just momentum from developers who started using CC when it was far superior to Codex has allowed it to become so much more popular. Potentially, it's might be that, as it's more autonomous, it's better for true vibe-coding and it's more popular with the Twitter/LinkedIn wantrepreneur crew which meant it gets a lot of publicity which increases adoption quicker.
Out of curiosity, what do you feel are the key differences between cursor + models versus something like Claude Code/Codex?
Are you feeling the benefits of the switch? What prompted you to change?
I've been running cursor with my own workflows (where planning is definitely a key step) and it's been great. However, the feeling of missing out, coupled with the fact I am a paying ChatGPT customer, got me to try codex. It hasn't really clicked in what way this is better, as so far it really hasn't been.
I have this feeling that supposedly you can give these tools a bit more of a hands-off approach so maybe I just haven't really done that yet. Haven't fiddled with worktrees or anything else yet either.
I'm with you. Codex's plans seems to be a much more generous offering than Claude
I just.. can't tell a different in quality between them.. so I go for the cheapest
Codex is great and I hit the usage once doing multiagent full 5 hour absolute degen session for the nornal workflow alongside never hit it and now x2 useage even and now with the planmode switch back and forth absolute great.
Terminal Bench 2.0
yea but i feel like we are over the hill on benchmaxxing, many times a model has beaten anthropic on a specific bench, but the 'feel' is that it is still not as good at coding
When Anthropic beats Benchmarks its somehow earned, when OpenAi games it, its somehow about not feeling good at coding.
'feel' is no more accurate
not saying there's a better way but both suck
The variety of tasks they can do and will be asked to do is too wide and dissimilar, it will be very hard to have a transversal measurement, at most we will have area specific consensus that model X or Y is better, it is like saying one person is the best coder at everything, that does not exist.
Yea, we're going to need benchmarks that incorporate series of steps of development for a particular language and how good each model is at it.
Like can the model take your plan and ask the right questions where there appear to be holes.
How wide of architecture and system design around your language does it understand.
How does it choose to use algorithms available in the language or common libraries.
How often does it hallucinate features/libraries that aren't there.
How does it perform as context get larger.
And that's for one particular language.
Speak for yourself. I've been insanely productive with Codex 5.2.
With the right scaffolding these models are able to perform serious work at high quality levels.
He wasn't saying that both of the models suck, but that the heuristics for measuring model capability suck
..huh?
The 'feel' of a single person is pretty meaningless, but when many users form a consensus over time after a model is released, it feels a lot more informative than a simple benchmark because it can shift over time as people individually discover the strong and weak points of what they're using and get better at it.
At the end of the day “feel” is what people rely on to pick which tool they use.
I’d feel unscientific and broken? Sure maybe why not.
But at the end of the day I’m going to choose what I see with my own two eyes over a number in a table.
Benchmarks are a sometimes useful to. But we are in prime Goodharts Law Territory.
yeah, to be honest it probably doesn't matter too much. I think the major models are very close in capabilities
I don’t think this is even remotely true in practice.
I honestly I have no idea what benchmarks are benchmarking. I don’t write JavaScript or do anything remotely webdev related.
The idea that all models have very close performance across all domains is a moderately insane take.
At any given moment the best model for my actual projects and my actual work varies.
Quite honestly Opus 4.5 is proof that benchmarks are dumb. When Opus 4.5 released no one was particularly excited. It was better with some slightly large numbers but whatever. It took about a month before everyone realized “holy shit this is a step function improvement in usefulness”. Benchmarks being +15% better on SWE bench didn’t mean a damn thing.
Your feeling is not my feeling, codex is unambiguously smarter model for me
Benchmarks are useless compared to real world performance.
Real world performance for these models is a disappoint.
Actually kind of excited for this. I've been using 5.2 for awhile now, and it's already pretty impressive if you set the context window to "high".
Something I have been experimenting with is AI-assisted proofs. Right now I've been playing with TLAPS to help write some more comprehensive correctness proofs for a thing I've been building, and 5.2 didn't seem quite up to it; I was able to figure out proofs on my own a bit better than it was, even when I would tell it to keep trying until it got it right.
I'm excited to see if 5.3 fairs a bit better; if I can get mechanized proofs working, then Fields Medal here I come!
> Using the develop web game skill and preselected, generic follow-up prompts like "fix the bug" or "improve the game", GPT‑5.3-Codex iterated on the games autonomously over millions of tokens.
I wish they would share the full conversation, token counts and more. I'd like to have a better sense of how they normalize these comparisons across version. Is this a 3-prompt 10m token game? a 30-prompt 100m token game? Are both models using similar prompts/token counts?
I vibe coded a small factorio web clone [1] that got pretty far using the models from last summer. I'd love to compare against this.
[1] https://factory-gpt.vercel.app/
I just wanted to say that's a pretty cool demo! I hadn't realised people were using it for things like this.
Thank you. There's a demo save to get the full feel of it quickly. There's also a 2D-ASCII and 3D render you can hotswap between. The 3D models are generated with Meshy. The entire game is 'AI slop'. I intentionally did no code reviews to see where that would get me. Some prompts were very specific but other prompts were just 'add a research of your choice'.
This was built using old versions of Codex, Gemini and Claude. I'll probably work on it more soon to try the latest models.
I've been listening to the insane 100x productivity gains you all are getting with AI and "this new crazy model is a real game changer" for a few years now, I think it's about time I asked:
Can you guys point me ton a single useful, majority LLM-written, preferably reliable, program that solves a non-trivial problem that hasn't been solved before a bunch of times in publicly available code?
I'm not sure if you'd call it a productivity gain, but I have to host our infrastructure on a system that runs processes entirely in Linux userland.
To bridge the containers in userland only, without root, I had to build: https://github.com/puzed/wrapguard
I'm sure it's not perfect, and I'm sure there are lots of performance/productivity gains that can be made, but it's allowed us to connect our CDN based containers (which don't have root) across multiple regions, talking to each other on the same Wireguard network.
No product existed that I could find to do this (at least none I could find), and I could never build this (within the timeframe) without the help of AI.
In the 1930s, when electronic calculators were first introduced, there was a widespread belief that accounting as a career was finished. Instead, the opposite became true. Accounting as a profession grew, becoming far more analytical/strategic than it had been previously.
You are correct that these models primarily address problems that have already been solved. However, that has always been the case for the majority of technical challenges. Before LLMs, we would often spend days searching Stack Overflow to find and adapt the right solution.
Another way to look at this is through the lens of problem decomposition as well. If a complex problem is a collection of sub-problems, receiving immediate solutions for those components accelerates the path to the final result.
For example, I was recently struggling with a UI feature where I wanted cards to follow a fan-like arc. I couldn't quite get the implementation right until I gave it to Gemini. It didn't solve the entire problem for me, but it suggested an approach involving polar coordinates and sine/cosine values. I was able to take that foundational logic turn it into a feature I wanted.
Was it a 100x productivity gain? No. But it was easily a 2x gain, because it replaced hours of searching and waiting for a mental breakthrough with immediate direction.
There was also a relevant thread on Hacker News recently regarding "vibe coding":
https://news.ycombinator.com/item?id=45205232
The developer created a unique game using scroll behavior as the primary input. While the technical aspects of scroll events are certainly "solved" problems, the creative application was novel.
The story you're describing doesn't seem much better than one could get from googling around and going on stackoverflow
It doesn’t have to be, really. Even if it could replace 30% of documentation and SO scrounging, that’s pretty valuable. Especially since you can offload that and go take a coffee.
I think the 'better than googling' part is less about the final code and more about the friction.
For example, consider this game: The game creates a target that's randomly generated on the screen and have a player at the middle of the screen that needs to hit the target. When a key is pressed, the player swings a rope attached to a metal ball in circles above it's head, at a certain rotational velocity. Upon key release, the player has to let go of the rope and the ball travels tangentially from the point of release. Each time you hit the target you score.
Now, I’m trying to calculate the tangential velocity of a projectile from a circular path, I could find the trig formulas on Stack Overflow. But with an LLM, I can describe the 'vibe' of the game mechanic and get the math scaffolded in seconds.
It's that shift from searching for syntax to architecting the logic that feels like the real win.
Why even come to this site if you're so anti-innovation?
Today with LLMs you can literally spend 5 minutes defining what you want to get, press send, go grab a coffee and come back to a working POC of something, in literally any programming language.
This is literally stuff of wonders and magic that redefines how we interface with computers and code. And the only thing you can think of is to ask if it can do something completely novel (that it's so hard to even quantity for humans that we don't have software patents mainly for that reason).
And the same model can also answer you if you ask it about maths, making you an itinerary or a recipe for lasagnas. C'mon now.
I don't think that the user you are responding to is anti-innovation, but rather points out that the usefulness of AI is oversold.
I'm using Copilot for Visual Studio at work. It is useful for me to speed some typing up using the auto-complete. On the other hand in agentic mode it fails to follow simple basic orders, and needs hand-holding to run. This might not be the most bleeding-edge setup, but the discrepancy between how it's sold and how much it actually helps for me is very real.
I think copilot is widely considered to be fairly rubbish, your description of agentic coding was also my experience prior to ~Q3 2025, but things have shifted meaningfully since then
There are different kinds of innovation.
I want AI that cures cancer and solves climate change. Instead we got AI that lets you plagiarize GPL code, does your homework for you, and roleplay your antisocial horny waifu fantasies.
> that hasn't been solved before a bunch of times in publicly available code?
And this matters because? Most devs are not working on novel never before seen problems.
Heh, I agree. There is a vast ocean of dev work that is just "upgrade criticalLib to v2.0" or adding support for a new field from the FE through to the BE.
I can name a few times where I worked on something that you could consider groundbreaking (for some values of groundbreaking), and even that was usually more the combination of small pieces of work or existing ideas.
As maybe a more poignant example- I used to do a lot of on-campus recruiting when I worked in HFT, and I think I disappointed a lot of people when I told them my day to day was pretty mundane and consisted of banging out Jiras, usually to support new exchanges, and/or securities we hadn't traded previously. 3% excitement, 97% unit tests and covering corner cases.
Well, it took opus 4.5 five messages to solve a trivial git problem for me. It hallucinated nonexistent flags three times. Hallucinating nonexistent flags is certainly a novel solution to my git ineptness.
Not to be outdone, chatgpt 5.2 thinking high only needed about 8 iterations to get a mostly-working ffmpeg conversion script for bash. It took another 5 messages to translate it to run in windows, on powershell (models escaping newlines on windows properly will be pretty nuch AGI, as far as I’m concerned).
Yeah, I would LOVE to see attempts at significant video games that are then open-sourced for communities to work on. E.g. OpenGTA or OpenFIFA/OpenNHL.
the 100x gains, even 10x, are obviously ridiculous but that doesn't mean AI is useless
baffled that people are still suspicious of ai coding models
Can you point me to a human written program an LLM cannot write? And no, just answering with a massively large codebase does not count because this issue is temporary.
Some people just hate progress.
> Can you point me to a human written program an LLM cannot write?
Sure:
"The resulting compiler has nearly reached the limits of Opus’s abilities. I tried (hard!) to fix several of the above limitations but wasn’t fully successful. New features and bugfixes frequently broke existing functionality.
As one particularly challenging example, Opus was unable to implement a 16-bit x86 code generator needed to boot into 16-bit real mode. While the compiler can output correct 16-bit x86 via the 66/67 opcode prefixes, the resulting compiled output is over 60kb, far exceeding the 32k code limit enforced by Linux. Instead, Claude simply cheats here and calls out to GCC for this phase (This is only the case for x86. For ARM or RISC-V, Claude’s compiler can compile completely by itself.)"[1]
1. https://www.anthropic.com/engineering/building-c-compiler
Pretty much any software that people pay for? If LLMs could clone an app, why would anyone still pay good money for the original?
Even a normal website like landonorris.com. Try copying all those effects with AI.
Another example: Red Dead Redemption 2
Another one: Roller coaster tycoon
Another one: ShaderToy
I wish I could agree with you, but as a game dev, shader author, and occasional asm hacker, I still think AIs have demonstrated being perfectly capable of copying "those effects". It's been trained on them, of course.
You're not gonna one-shot RD2, but neither will a human. You can one-shot particles and shader passes though.
I didnt say one shot it, coding agents have been out for more than couple years and yet we cant point to single Good piece of software built by it.
Why do you believe an LLM can't write these, just because they're 3D? If the assets are given (just as with a human game programmer, who has artists provide them the assets), then an LLM can write the code just the same.
What? People can easily get assets, thats not a even a problem in 2026. Roller coaster tycoon's assets were done by the programmer himself. If its so easy why haven't we seen actually complex pieces of software done in couple of weeks by LLM users?
Also try building any complex effects by prompting LLMs, you wont get any far, this is why all of the LLM coded websites look stupidly bland.
Not sure what you're confused about, I never said assets were hard to get, I just said that the LLM needs to be provided a folder of the assets for it to make use of them, it's not going to create them from scratch (at least not without great difficulty, because LLMs are capable of using and coding Three.js for example). I don't know the answer to your first question because I don't hang around in the 3D or game dev fields, I'm sure there are examples of vibe coded games however.
As to your second question, it is about prompting them correctly, for example [0]. Now I don't know about you but some of those sites especially after using the frontend skill look pretty good to me. If those look bland to you then I'm not really sure what you're expecting, keeping in mind that the example you showed with the graphics are not regular sites but more design oriented, and even still nothing stops LLMs from producing such sites.
[0] https://youtu.be/f2FnYRP5kC4
you have shown me 0 examples, I showed actual examples to the given question. Your answers have just been "AI can also do this" but gave no actual proof.
The examples are in the video I linked, as I said, if you don't bother to watch it then I'm not sure what to tell you. As I said for games I don't know and won't presume to search up some random vibe coded game if I don't have personal experience with how LLMs handle games, but for web development, the sites I've made and seen made look pretty good.
And some people clearly hate humans.
Great question, here is the link from the future:
Do software engineers here feel threatened by this? I certainly am. I'm surprised that this topic is almost entirely missing in these threads.
No. AI does not work well enough, you still need a person to look on it and CODE. It probably never will, until AGI which probably also in my opinion will never come.
It's a super-special AI tier that can replace developers and other grunts, yet somehow cannot replace managers and C-suite.
It can only replace whoever is not writing a fat cheque to it.
AI is mostly garbage at creating useful abstractions. I'd feel threatened if I was a competitive programmer or IMO kid
theres alot of denial, and people that havent taken a serious look at the ai models
I would feel threatened if I didn't invest in learning how to best use AI
Jevons paradox hints that the situation is not as bleak as it sounds.
When 2 multi billion giants advertise same day, it is not competition but rather a sign of struggle and survival. With all the power of the "best artificial intelligence" at your disposition, and a lot of capital also all the brilliant minds, THIS IS WHAT YOU COULD COME UP WITH?
Interesting
Yeah they are both fighting for survival. No surprise really.
Need to keep the hype going if they are both IPO'ing later this year.
The AI market is an infinite sum market.
Consider the fact that 7 year old TPUs are still sitting at near 100p utilization today.
How many IPOs can a company really do?
As many as they want. They can "spin off" and then "merge" again.
What happened to you?
AI fried brains, unfortunately.
I mean, he has a point it’s just not very eloquently written.
I empathize with the situation, no elegance from them, no eloquence from me :)
What's funny is that most of this "progress" is new datasets + post-training shaping the model's behavior (instruction + preference tuning). There is no moat besides that.
"post-training shaping the models behavior" it seems from your wording that you find it not that dramatic. I rather find the fact that RL on novel environments providing steady improvements after base-model an incredibly bullish signal on future AI improvements. I also believe that the capability increase are transferring to other domains (or at least covers enough domains) that it represents a real rise in intelligence in the human sense (when measured in capabilities - not necessarily innate learning ability)
What evidence do you base your opinions on capability transfer on?
>There is no moat besides that.
Compute.
Google didn't announce $185 billion in capex to do cataloguing and flash cards.
Google didn't buy 30% of Anthropic to starve them of compute
Probably why it's selling them TPUs.
> is new datasets + post-training shaping the model's behavior (instruction + preference tuning). There is no moat besides that.
sure, but acquiring/generating/creating/curating so much high quality data is still significant moat.
Many are saying codex is more interactive but ironically I think that very interactivity/determinism works best when using codex remotely as a cloud agent and in highly async cases. Conversely I find opus great locally, where I can ram messages into it to try to lever its autonomy best (and interrupt/clean up)
The behind the scenes on deciding when to release these models has got to be pretty insanely stressful if they're coming out within 30 minutes-ish of each other.
I wonder if their "5.3" was continuously being updated, with regenerated benchmarks with each improvement, and they just stayed ready to release it when claude released
This seems plausible. It would be shocking if these companies didn't have an automated testing suite which is recomputing these benchmarks on a regular basis, and uploading to a dashboard for supervision.
Given that they already pre-approved various language and marketing materials beforehand there's no real reason they couldn't just leave it lined up with a function call to go live once the key players make the call.
It’s also functionally not likely without some sort of insider knowledge or coordination
Could be, could also be situations where things are lined up to launch in the near future and then a mad dash happens upon receiving outside news of another launch happening.
I suppose coincidences happen too but that just seems too unlikely to believe honestly. Some sort of knowledge leakage does seem like the most likely reason.
Using opus 4.6 in claude code right now. It's taking about 5x longer to think things through, if not more.
The notes explicitly call out you may want to dial the effort setting back to medium to reduce latency/tokens (high being default, apparently there is a max setting too).
I would love to see a nutritional facts label on how many prompts / % of code / ratio of human involvement needed to use the models to develop their latest models for the various parts of their systems.
I want to recompile a Rust project to be f32 instead of f64.
Am I better off buying 1 month of Codex, Claude, or Antigravity?
I want to have the agent continuesly recompile and fix compile errors on loop until all the bugs from switching to f32 are gone.
If I'm not mistaken Codex is free until April 2nd with the previous generous rate limits (while paying customers get 2x).
Literally just find and replace
find and replace is step 1 that generates all the compile errors I want it to loop through
I'm wanting to do it on an entire programming language made in rust: https://github.com/uiua-lang/uiua
Because there are no float32 array languages in existence today
Doesn't matter which one. All of them can do things like this now, given a good enough feedback loop. Which your problem has.
All of them can do it but Codex has the least frustrating usage limits.
When using it in VSCode? The browser system running its own container seems like it would be the most demanding on their resources. The stand-alone client is Mac-only but I don't know if it makes a difference.
My goal is to do it within the usage I get from a $20 monthly plan.
Both Opus 4.6 and GPT-5.3 one shot a Gameboy emulator for me. Guess I need a better benchmark.
There's hundreds of gameboy emulators available on Github they've been trained on. It's quite literally the simplest piece of emulation you could do. The fact that they couldn't do it before is an indictment of how shit they were, but a gameboy emulator should be a weekend project for anyone even ever so slightly qualified. Your benchmark was awful to begin with.
Is such an emulator not part of their training data sets?
As coding agents get "good enough" the next differentiator will be which one can complete a task in fewer tokens.
Or quicker, or more comprehensively for the same price.
Or the same number of tokens in less time. Kinda feels like the CPU / modem wars of the 90s all over again - I remember those differences you felt going from a 386 -> 486 or from a 2400 -> 9600 baud modem.
We're in the 2400 baud era for coding agents and I for one look forward to the 56k era around the corner ;)
> our team was blown away > by how much Codex was able > to accelerate its own development
they forgot to add “Can’t wait to see what you do with it”
> GPT‑5.3-Codex was co-designed for, trained with, and served on NVIDIA GB200 NVL72 systems. We are grateful to NVIDIA for their partnership.
This is hilarious lol
How so?
They're on shaky ground right now https://arstechnica.com/information-technology/2026/02/five-...
Its kind of a suck up that more or less confirms the beef stories that were floating around this past week.
In case you missed it. For example:
Nvidia's $100 billion OpenAI deal has seemingly vanished - Ars Technica
https://arstechnica.com/information-technology/2026/02/five-...
Specifically this paragraph is what I find hilarious.
> According to the report, the issue became apparent in OpenAI’s Codex, an AI code-generation tool. OpenAI staff reportedly attributed some of Codex’s performance limitations to Nvidia’s GPU-based hardware.
There was never a $100 billion deal. Only a letter of intent which doesn't mean anything contractually.
> OpenAI staff reportedly attributed some of Codex’s performance limitations to Nvidia’s GPU-based hardware.
They should design their own hardware, then. Somehow the other companies seem to be able to produce fast-enough models.
For those who cared:
GPT-5.3-Codex dominates terminal coding with a roughly 12% lead (Terminal-Bench 2.0), while Opus 4.6 retains the edge in general computer use by 8% (OSWorld).
Anyone knows the difference between OSWorld vs OSWorld Verified?
From Claude 4.6 Thinking:
OSWorld is the full 369-task benchmark. OSWorld Verified is a ~200-task subset where humans have confirmed the eval scripts reliably score success/failure — the full set has some noisy grading where correct actions can still get marked wrong.
Scores on Verified tend to run higher, so they're not directly comparable.
Did they post the knowledge cutoff date somewhere
It's here: https://platform.claude.com/docs/en/about-claude/models/over...
Reliable knowledge cutoff: May 2025, training data cutoff: August 2025
i like the opus 4.6 announcement a lot more, concise and to the point. for the 5.3 codex, it's a long post, but still, the most important info, the context window, is nowhere to be found. thus, I'm keeping using opus.
Model card: https://cdn.openai.com/pdf/23eca107-a9b1-4d2c-b156-7deb4fbc6...
I think models are smart enough for most of the stuff, these little incremental changes barely matter now. What I want is the model that is fast.
This is faster if their marketing is right, it uses significantly less tokens. Gemini 3 flash is very good as well.
I'm having a hard time parsing the openai website.
Anyone know if it is possible to use this model with opencode with the plus subscription?
Gotta love how the game demo's page title is "threejs" – I guess the point was to demo its vibe-coding abilities anyway, but yea..
Anthropic mostly had an advantage in speed. It feels like with a 25% increase in speed with Codex 5.3, they are now losing that advantage as well.
I just asked Opus 4.6 to debug a bug in my current changes and it went for 20 minutes before I interrupted it. Take that as you will.
Doesn't feel like a useful data point without more context. For some hard bugs I'd be thrilled to wait 30 minutes for a fix, for a trivial CSS fix not so much. I've spent weeks+ of my career fix single bugs. Context is everything.
Sure, but I've never experienced a 20 minute wait with CC before. It was an architectural question but it would have taken a couple minutes with a definitive answer on 4.5.
AI designed websites are so easy to spot that I need to actively design my UI so that it doesn't look AI
I never really used Codex (found it to slow) just 5.2, which I going to be an excellent model for my work. This looks like another step up.
This week, I'm all local though, playing with opencode and running qwen3 coder next on my little spark machine. With the way these local models are progressing, I might move all my llm work locally.
I think codex got much faster for smaller tasks in the last few months. Especially if you turn thinking down to medium.
I think the slow feeling is a UI thing in codex
I realize my comment was unclear. I use codex the CLI all the time, but generally with this invocation: `codex --full-auto -m gpt-5.2`
However, when I use the 5.2codex model, I've found it to be very slow and worse (hard to quantify, but I preferred straight-up 5.2 output).
It's so difficult to compare these models because they're not running the same set of evals. I think literally the only eval variant that was reported for both Opus 4.6 and GPT-5.3-Codex is Terminal-Bench 2.0, with Opus 4.6 at 65.4% and GPT-5.3-Codex at 77.3%. None of the other evals were identical, so the numbers for them are not comparable.
Isn't the best eval the one you build yourself, for your own use cases and value production?
I encourage people to try. You can even timebox it and come up with some simple things that might look initially insufficient but that discomfort is actually a sign that there's something there. Very similar to moving from not having unit/integration tests for design or regression and starting to have them.
I usually wait to see what ArtificialAnalysis says for a direct comparison.
It's better on a benchmark I've never heard of!? That is groundbreaking, I'm switching immediately!
I also wasn't that familiar with it, but the Opus 4.6 announcement leaned pretty heavily on the TerminalBench 2.0 score to quantify how much of an improvement it was for coding, so it looks pretty bad for Anthropic that OpenAI beat them on that specific benchmark so soundly.
Looking at the Opus model card I see that they also have by far the highest score for a single model on ARC-AGI-2. I wonder why they didn't advertise that.
No way! Must be a coinkydink, no way OpenAI knew ahead of time that Anthropic was gonna put a focus on that specific useless benchmark as opposed to all the other useless benchmarks!?
I'm firing 10 people now instead of 5!
I find it very, very interesting how they demoed visuals in the form of the “soft SaaS” website and mentioned how it can do user research. Codex has usually lagged behind Claude and Gemini when it comes to UX, so I’m curious to see if 5.3 will take the lead in real world use. Perhaps it’ll be available in Figma Make now?
I’m hoping they add better IDE integration to track active file and selection. That’s the biggest annoyance I have in working with Codex.
gpt-5.3-codex isn't available on the API yet. From TFA:
> We are working to safely enable API access soon.
GPT-5.2-Codex was so cool at price/value rate, hope 5.3 will not ruin the race with claude
May AI not write the code for me.
May I at least understand what it has "written". AI help is good but don't replace real programmers completely. I'm enough copy pasting code i don't understand. What if one day AI will fall down and there will be no real programmers to write the software. AI for help is good but I don't want AI to write whole files into my project. Then something may broke and I won't know what's broken. I've experienced it many times already. Told the AI to write something for me. The code was not working at all. It was compiling normally but the program was bugged. Or when I was making some bigger project with ChatGPT only, it was mostly working but after a longer time when I was promting more and more things, everything got broken.
Honest question: have you tried evolving your code architecture when adding features instead of just "promting more and more things"?
I've tried that too but it was almost the same, chatgpt kept forgetting many things about the code and project structure. In summary AI can get problematic for me and i get with troubles with it. This is like one of the reasons why I still prefer traditional text editor for writing code like Vim over a "software on steroids" like VS Code and things like that...
> What if one day AI will fall down and there will be no real programmers to write the software.
What if you want to write something very complex now that most people don't understand? You keep offering more money until someone takes the time to learn it and accomplish it, or you give up.
I mean, there are still people that hammer out horseshoes over a hot fire. You can get anything you're willing to pay money for.
Sorry but companies will not hire you but instead a person who learned how to code with AI. Get with the times or lose.
I'm afraid of all of the modern world especially in technology, I guess if now I would "come back" to all of modern and new things: the commercialized world, AI, corporations, etc...my head would explode. I mean I can't imagine living in such world. I am not sure if everything would be alright eith myself in all this everything,This is just too much...
It's that Austin Powers clip of the guy slowly getting smooshed by the steam roller.
That was fast!
I really do wonder whats the chain here. Did Sam see the Opus announcement and DM someone a minute later?
OpenAI has a whole history of trying to scoop other providers. This was a whole thing for Google launches, where OpenAI regularly launched something just before Google to grab the media attention.
Some recent examples:
GPT-4o vs. Google I/O (May 2024): OpenAI scheduled its "Spring Update" exactly 24 hours before Google’s biggest event of the year, Google I/O. They launched GPT-4o voice mode.
Sora vs. Gemini 1.5 Pro (Feb 2024): Just two hours after Google announced its breakthrough Gemini 1.5 Pro model, Sam Altman tweeted the reveal of Sora (text-to-video).
ChatGPT Enterprise vs. Google Cloud Next (Aug 2023): As Google began its major conference focused on selling AI to businesses, OpenAI announced ChatGPT Enterprise.
I assume some sort of corporate espionage. This is high stakes after all
Tell me that you are hurt without telling me that you are hurt this applies to Sam right now
Having used codex a fair bit I find it really struggles with … almost anything. However using the equivalent chat gpt model is fantastic. I guess it’s a matter of focus and being provided with a smaller set of code to tackle.
Interesting that this was released without a prior GPT-5.3 release. I wonder if that means we won't see a GPT-5.3?
Anyone remember the dot-com era when you would see one provider claim the most miles of fibre and then later that week another would have the title?
Funny that this and Opus 4.6 released within minutes of each other. Each showing similar score improvements. Each claiming to be revolutionary.
I've been using 5.2 the way they're describing the new use case for 5.3 this whole time.
is so fun that the two releases used almost completely non-overlapping benchmarks!
I think this announcement says a lot about OpenAI and their relationship to partners like Microsoft and NVIDIA, not to mention the attitude of their leadership team.
On Microsoft Foundry I can see the new Codex 4.6 model right now, but GPT-5.3 is nowhere to be seen.
I have a pre-paid account directly with OpenAI that has credits, but if I use that key with the Codex CLI, it can't access 5.3 either.
The press release very prominently includes this quote: "GPT‑5.3-Codex was co-designed for, trained with, and served on NVIDIA GB200 NVL72 systems. We are grateful to NVIDIA for their partnership."
Sounds like OpenAI's ties with their vendors are fraying while at the same time they're struggling to execute on the basics like "make our own models available to our own coding agents", let alone via third-party portals like Microsoft Foundry.
GPT 5.3 is not in the API yet AIUI.
At first try it solved a problem that 5.2 couldn't previously.
Seems to be slower/thinks longer.
How'd they both release at the same time? Insiders?
So can I use this from Opencode? Because Anthropic started to enforce their TOS to kill the Opencode integration
OpenAI models in general, yes - `opencode auth login`, select OpenAI, then ChatGPT Pro/Plus. I just checked and 5.3-codex isn't available in opencode yet, but I assume it will be soon.
You can also use via Opencode Zen, Github Copilot, or probably any number of other model providers that Opencode integrates with.
Not sure why everyone stays focused on getting it from Anthropic or OpenAI directly when there are so many places to get access to these models and many others for the same or less money.
I've tried opus 4.5 in opencode via the GitHub Copilot API, mostly to see if it works all. I don't think that broke any terms of service? But also I haven't checked how much more expensive I made it for myself over just calling them directly.
You can use Anthropic models in Opencode, make an api key and you're good to do(you can even use the in house Opencode router, Zen).
What you can't do is pretend opencode is claude code to make use of that specific claude code subscription.
Yes, OpenAI said they'd allow usage of their subscriptions in opencode.
The most important question: Can it do Svelte now?
Today is the best day to rewrite everything in React. You may not enjoy React, but AI agents do. And they are the ones writing the code.
5.2 was already very good with svelte 5, at least when you have the svelte MCP server set up.
Where is the google?
gemini-3-flash-preview will be GA soon i hope. /s
Any notes on pricing?
It's not in the API yet - "We are working to safely enable API access soon.", but I assume the rate-limits won't be worse than for 5.2 Codex.
Ah, "It's ready, but not yet".
You can just use it outside of the API?
I am on a max subscription for Claude, and hate the fact that OpenAI have not figured out that $20 => $200 is a big jump. Good luck to them. In terms of model, just last night, Codex 5.2 solved a problem for me which other models were going round and round. Almost same instructions. That said, I still plan to be on $100 Claude (overall value across many tasks, ability to create docs, co-work), and may bump up OpenAI subscription to the next tier should they decide to introduce one. Not going to $200 even with 5.3, unless my company pays for it.
I'm coding about 6-9h per day with Codex CLI on the $20 Plus sub, occasionally switching to extra-high reasoning and feeding it massive contexts, all tools enabled, sometimes 2-3 terminal sessions running in parallel and I've never hit limits... I operate on small-ish codebases but even so I try to work in the most local scope possible with AGENTS.md at the sub-directory levels.
Are you really hitting limits, or are you turned off by the fact you think you will?
You are correct :-) I am turned off by the fact that I will hit the limit if I used more. But you gave me confidence. I guess $20 can go a long way. I think only once in the last 3 months I got rate limited in Codex.
You should look into Kilo Pass by Kilo Code (https://kilo.ai/features/kilo-pass). It's basically a fixed subscription and your credits roll over each month, and you get free extra credits too which are used up first before paid credits. It's similar to paying for Cursor except the credits roll over which is why I'm contemplating moving to it, because I don't want to be locked into any one LLM provider the way Claude Code or Codex make you become.
I guess the jump is on purpose. You can buy Codex credits and also use codex via the API (manual switching required).
I use Codex in OpenCode through the API and find the experience quite enjoyable.
Need to try OpenCode. Thanks.
It seems Fast!
According to Sam Altman, Anthropic is for "rich people." Judging by his $4 million man-baby Koeniggsegg, he must be a huge Claude Code user!
He would be! If they didn't get banned from using it in the OAI offices. He's so mad.
I'd like to know if and how much illegal use of customer prompts are used for training.
"But we anonymize prompts before training!"
Meanwhile the prompt: Crop this photo of my passport
Oh yeah that’s in the “These Are The Illegal Things We Did” section 7.4 in the Model Card.
Does it insert adverts in your code?
what are the benchmarks against opus 4.6?
Whoa, I think GPT-5.2-Codex was a disappointment, but GPT-5.3-Codex is definitely the future!
Anybody else not seeing it available in Codex app or CLI yet (with Plus)?
My codex CLI didn’t notice version bump available, but I manually did pnpm add -g @openai/codex and 5.3 was there after.
Anthropic and GTP 2 new models at once?
Pelican seems much worse than the Opus 4.6 one (though the bicycle is more accurate):
https://gist.github.com/simonw/a6806ce41b4c721e240a4548ecdbe...
It is absurd to release 5.3-Codex before first releasing 5.3.
Also, there is no reason for OpenAI and Anthropic to be trying to one-up each other's releases on the same day. It is hell for the reader.
Because Claude Code is stealing the thunder so OpenAI is focusing on coding now.
Yeah, Claude Code is what everyone is talking about these days and since OpenAI has always been the spending driver being 2nd or 3rd fiddle just isn't acceptable if they're gonna justify it.
That is where the money is.
This. I think software development is the best usecase for AI yet. I use it almost daily at work and it's a huge help.
Enterprise customers will happily pay even 100$/mo subscriptions and it has a clear value proposition that can be decently verified.
It's where the revenue is, but it isn't going to be where the profit is. Developers will easily use absurdly large amounts of compute, costing the AI provider a lot more than they receive in revenue.
Why is it absurd?
I agree, I was confused about where 5.3 non Codex was. 5.2-Codex disappointed me enough that I won't be giving 5.3 Codex a try, but I'm looking forward to trying 5.3 non Codex with Pi.
GPT-5.x in general are very disappointing, the only good chat model was GPT-5 in the first week before they made "the personality warmer" and Codex in general was always kinda meh.
Almost like Anthropic and OpenAI are trying to front run each other
lmao so cringe that they delay releasing the model until anthropic does
I know we just got a reset and a 2× bump with the native app release, but shipping 5.3 with no reset feels mismatched. If I’d known this was coming, I wouldn’t have used up the quota on the previous model.
Is this me or Sam is being absolute sore loser he is and trying to steal Opus thunder?
Why is it loser? He very well could be a sore winner here.
OpenAI is still the only AI company that has structured outputs. Anthropic now supports JSON schema but you can't specify array length.
Google Gemini definitely has structured output.
Can you elaborate what you mean - OAI structured outputs means JSON schema doesn't it? So are you just saying they both support JSON schema but Anthropic has a limitation?
OpenAI, in addition to JSON schema, supports "context-free grammar"[0], i.e. regex and lark. Anthropic also supports JSON schema since a few weeks ago, but they don't support specifying the length of JSON array, so you still have to worry about the model producing invalid output.
[0]: https://platform.openai.com/docs/guides/function-calling#con...
One thing that pisses me off is this widespread misunderstanding that you can just fall back to function calling (Anthropic's function calling accepts JSON schema for arguments), and that it's the same as structured outputs. It is not. They just dump the JSON schema into the context without doing the actual structured outputs. Vercel's AI SDK does that and it pisses me off because doing that only confuses the model and prefilling works much better.
They both are doing this to each other.
BTW, loser is spelled with a single o.
You could also claim that Anthropic is trying to scoop OpenAI by launching minutes earlier, as OpenAI has done with Google in the past.
For downvoters, you must be naive to think these companies are not surveilling each other through various means.