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.
You might be able to get away without the review step for a bit, but eventually (and not long) you will be bitten.
It's not a waste of time, it's a responsibility. All things need steering, even humans -- there's only so much precision that can be extrapolated from prompts, and as the tasks get bigger, small deviations can turn into very large mistakes.
There's a balance to strike between micro-management and no steering at all.
If it really knows better, then fire everyone and let the agent take charge. lol
This sounds like never. Most businesses are still shuffling paper and couldn’t give you the requirements for a CRUD app if their lives depended on it.
You’re right, in theory, but it’s like saying you could predict the future if you could just model the universe in perfect detail. But it’s not possible, even in theory.
If you can fully describe what you need to the degree ambiguity is removed, you’ve already built the thing.
If you can’t fully describe the thing, like some general “make more profit” or “lower costs”, you’re in paper clip maximizer territory.
That could easily be automated.
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 don’t think there’s something deeply philosophical in here, especially as Claude Code is pushing stronger for asking more questions recently, introduced functionality to “chat about questions” while they’re asked, etc.
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.
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.
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 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.
https://gist.github.com/drorm/7851e6ee84a263c8bad743b037fb7a...
I typically use github issues as the unit of work, so that's part of my instruction.
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.
When such benchmarks aren’t available what you often get instead is teams creating their own benchmark datasets and then testing both their and existing models’ performance against it. Which is eve worse because they probably still the rest multiple times (there’s simply no way to hold others accountable on this front), but on top of that they often hyperparameter tune their own model for the dataset but reuse previously published hyperparameters for the other models. Which gives them an unfair advantage because those hyperparameters were tuned to a doffeeent dataset and may not have even been optimizing for the same task.
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 .
AI agents, perhaps? :-D
You can take off your tinfoil hat. The same models can perform differently depending on the programming language, frameworks and libraries employed, and even project. Also, context does matter, and a model's output greatly varies depending on your prompt history.
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!
Ah and let me guess all your frontends look like cookie cutter versions of this: https://openclaw.dog/
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.
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.
Like, it can't even answer the phone.
that's certainly one way to refer to Scott Alexander
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
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.
And yet RAM prices are still sky high. Game consoles are getting more expensive, not cheaper, as a result. When will competition benefit those consumers? Or consumers of desktop RAM?
However, the investors currently subsidizing those wins to below cost may be getting huge losses.
There aren't any insurmountable large moats, plenty of open weight models that perform close enough.
> CO₂ emissions
Different industry that could also benefit from more competition ? Clean(er) energy is not even more expensive than dirty sources on pure $/kWh, we still do need dirty sources for workloads like base demand, peakers etc that the cheap clean sources cannot service today.
[1] https://en.wikipedia.org/wiki/United_States_antitrust_law
---
Sadly it was the core of anti-trust law, since 1970s things have changed.
The predominant view today (i.e. Chicago School view) in both judiciary and executive are influenced by Justice Bork's ideas that consumer benefit being the deciding factor over company's actions.
Consumer benefits becomes opinions of projections by either side of a case about the future, whereas company actions like collusion, pricing fixing or M&A are hard facts with strong evidence. Today it is all vibes on how the courts (or executive) feel .
So now we have Government sanctioned cartels like in Aviation Alliances [1] that is basically based on convoluted catch-22-esque reasoning because it favors strategic goals even though it would be a violation of the letter/spirit of the law.
[1] https://www.transportation.gov/office-policy/aviation-policy...
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.
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.
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 just.. can't tell a different in quality between them.. so I go for the cheapest
| Name | Score |
|---------------------|-------|
| OpenAI Codex 5.3 | 77.3 |
| Anthropic Opus 4.6 | 65.4 |not saying there's a better way but both suck
With the right scaffolding these models are able to perform serious work at high quality levels.
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.
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.
This was built using old versions of Codex, Gemini and Claude. I'll probably work on it more soon to try the latest models.
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.
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.
...This may still be worth it. In any case it will stop being a problem once the human is completely out of the loop.
edit: but personally I hate missing out on the chance to learn something.
Today, I know very well how to multiply 98123948 and 109823593 by hand. That doesn't mean I will do it by hand if I have a calculator handy.
Also, ancient scholars, most notably Socrates via Plato, opposed writing because they believed it would weaken human memory, create false wisdom, and stifle interactive dialogue. But hey, turns out you learn better if you write and practice.
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.
To deny at least that level of productivity at this point, you have to have your head in the sand.
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.
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.
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).
I see this originality criteria appended a lot, and
1) I don't think it's representative of the actual requirements for something to be extremely useful and productivity-enhancing, even revolutionary, for programming. IDE features, testing, code generation, compilers — all of these things did not really directly help you produce more original solutions to original problems, and yet they were huge advances in program or productivity.
I mean like. How many such programs are there in general?
The vast vast majority of programs that are written are slight modifications, reorganizations, or extensions, of one or more programs that are already publicly available a bunch of times over.
Even the ones that aren't could fairly easily be considered just recombinations of different pieces of programs that have been written and are publicly available dozens or more times over, just different parts of them combined in a different order.
Hell, most code is a reorganization or recombination of the exact same types of patterns just in a different way corresponding to different business logic or algorithms, if you want to push it that far.
And yet plenty of deeply unoriginal programs are very useful and fill a useful niche, so they get written anyway.
2) Nor is it a particularly satisfiable goal. If there aren't, as a percentage, very many reliable, useful, and original programs that have been written in the decades since open source became a thing, why would we expect a five-year-old technology to have done so, especially when, obviously, the more reliable original and broadly useful programs have already written, the narrower the scope for new ones to satisfy the originality criteria?
3) Nor is it actually something that we would expect even under the hypothesis that agents make people significantly more productive at programs. Even if agents give 100x productivity gains to writing a useful tool or service or program or improving existing ones with new features. We still wouldn't expect them to give necessarily very many much productivity gains at all to writing original programs, precisely because of their current technology is a product of deep thinking, understanding a specific domain, seeing a niche, inspiration, science, talent and luck much more than the ability to even do productive engineering.
But I have plenty of examples of really atrocious human written code to show you! TheDailyWtf has been documenting the phenomenon for decades.
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.
Edit: I found examples [0] of games too with generated assets as well. These are all one shot so I imagine with more prompting you can get a decent game all without coding anything yourself.