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.
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.
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
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.
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 constantly reminders.
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.
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.
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'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.
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.
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.
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!
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?
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.
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.
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)
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.
> 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.
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)
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.
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.
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.
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).
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.
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...
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.
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.
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.
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 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.
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 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 constantly reminders.
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 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 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.
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.
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...
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
Theres hundreds of people who upload Codex 5.2 running for hours unattended and coming back with full commits
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.
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.
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.
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.
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.
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.
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 .
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?)
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!
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.
Ah and let me guess all your frontends look like cookie cutter versions of this: https://openclaw.dog/
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.
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.
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.
Hopefully performance will pick up after the rollout.
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.
https://www.nbcnews.com/tech/tech-news/openai-releases-chatg...
I wonder if this will continue to be the case.
"We added some more ACLs and updated our regex"
> 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.
- "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".
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.
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.
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 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
https://x.com/TheZvi/status/2017310187309113781
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.
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.
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.
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.
I just.. can't tell a different in quality between them.. so I go for the cheapest
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.
>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.
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.
Model costs continue to collapse while capability improves.
Competition is fantastic.
However, the investors currently subsidizing those wins to below cost may be getting huge losses.
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.
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.
When the welfare state, enabled by technology, falls apart, it won't take long for European society to fall apart. Except France maybe.
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!
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?
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.
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'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 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.
And this matters because? Most devs are not working on novel never before seen problems.
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.
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).
Some people just hate progress.
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
Another example: Red Dead Redemption 2
Another one: Roller coaster tycoon
Another one: ShaderToy
You're not gonna one-shot RD2, but neither will a human. You can one-shot particles and shader passes though.
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.
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
not saying there's a better way but both suck
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.
With the right scaffolding these models are able to perform serious work at high quality levels.
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.
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.
Real world performance for these models is a disappoint.
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/
This was built using old versions of Codex, Gemini and Claude. I'll probably work on it more soon to try the latest models.
Interesting
Need to keep the hype going if they are both IPO'ing later this year.
Consider the fact that 7 year old TPUs are still sitting at near 100p utilization today.
Compute.
Google didn't announce $185 billion in capex to do cataloguing and flash cards.
sure, but acquiring/generating/creating/curating so much high quality data is still significant moat.
It can only replace whoever is not writing a fat cheque to it.
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.
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.
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.
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
My goal is to do it within the usage I get from a $20 monthly plan.
We're in the 2400 baud era for coding agents and I for one look forward to the 56k era around the corner ;)
they forgot to add “Can’t wait to see what you do with it”
This is hilarious lol
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.
They should design their own hardware, then. Somehow the other companies seem to be able to produce fast-enough models.
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.
Anyone know if it is possible to use this model with opencode with the plus subscription?
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.
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).
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.
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.
I'm firing 10 people now instead of 5!
> We are working to safely enable API access soon.
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.
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 really do wonder whats the chain here. Did Sam see the Opus announcement and DM someone a minute later?
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.
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.
Seems to be slower/thinks longer.
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.
What you can't do is pretend opencode is claude code to make use of that specific claude code subscription.
Are you really hitting limits, or are you turned off by the fact you think you will?
Meanwhile the prompt: Crop this photo of my passport
https://gist.github.com/simonw/a6806ce41b4c721e240a4548ecdbe...
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.
Enterprise customers will happily pay even 100$/mo subscriptions and it has a clear value proposition that can be decently verified.
[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.
BTW, loser is spelled with a single o.
For downvoters, you must be naive to think these companies are not surveilling each other through various means.