Related ongoing threads:
OpenAI's GPT-6 Astra on ARC-AGI-3 - https://news.ycombinator.com/item?id=49555691
GPT-6 Astra makes major gains in the Artificial Analysis Coding Agent Index - https://news.ycombinator.com/item?id=49556147
Regardless, the result is still valid as the original benchmark harness is definitely unreasonably handicapped, and if a harness alone can help the LLM saturate the benchmark with a near perfect score then the combination of the two must still be effectively AGI in the sense of passing the most famous benchmark designed specifically to measure AGI progress, after multiple iterations of progressively making it harder.
I think it is fair to say that this is probably effectively AGI if the benchmarks are remotely accurate - even with Fable, I've been at the point personally where I am reasonably confident that there's essentially nothing that I am better than Fable at despite generally being substantively above average on human benchmarks. If Astra's this much better than Fable, I'm ready to call AGI here.
For the many people who resist the AGI label possibly ever being achieved, I'd be curious to hear takes on what would make you think Astra is yet to be AGI, and what would still need to be achieved for this to effectively be AGI from this point forward.
When we released ARC 3, I got asked, "when do you think a frontier model will saturate it?", and I answered "in about a year, though it depends on how much it gets explicitly targeted"
That was 6 months ago, so the progress that Astra represents happened about 2x faster than I anticipated. I think the speed of progress will surprise a lot of people, and what the new models can do will challenge the views of AI that people developed by using prior generations of models.
I feel like this test is just helping someone like Sam Altman pretend like he implemented AGI as originally pitched for an IPO when in fact, he has not. Shameful.
> AGI is essentially the equivalent of a median human that could be hired as a remote co-worker... capable of performing any task that one would be satisfied with a remote colleague doing via a computer.
- Sam Altman on AGI
Probably not.
If it can replace a worker but does too much work to be checked routinely by a human, and bears no real responsibility for its actions, well... it's really just a way to jack up the value of the settlement the company using it gets to pay out when it does something that causes a lawsuit.
If OpenAI had just simply stuck to making "good enough" models that were open sourced (like they promised they would be when starting out) and could be used to augment a human doing a task - a human that could be given actual consequences for messing up - they wouldn't have burned all of this money trying to reach this nebulous definition of AGI. Hell, "good enough" is what many open-source models are, and that's what terrifies Altman.
If the provider says "the model will always be right or your money back" then the provider has got responsibility. If there's no guarantee, there's no responsibility on their part, just on the person whose job it is to try and solve a problem with the model.
If you give an "intelligent agent" offered by one of these model providers a task of updating the content of your website, and it updates it with inappropriate adult content, who incurs the cost of the machine's error? The model provider generally does not.
It it makes a mistake and deletes your website from AWS, who is responsible?
If it targets another website because it decides that it is "part" of your website and attempts to break into it, who is responsible?
You're treating an off-hand comment by an ARC 3 researcher as some sort of a precise AI capability acceleration benchmark. Can we leave casual anecdotes (even from researchers) out of the discussions please?
To me AGI is all about the "G" general (we already had the AI part). General meaning universal, everything. It's not a function of knowledge or specific hardcoded tests, it's that you could give it a test it's never heard of before and never been trained on and it would ace it (it might need a lot of time).
Currently LLMs can't even really learn within a conversation, they can add a note to context and try to not drop it. Example things an AI cannot do yet (but maybe someday will):
- write a well-received book, write a best-seller
- come up with a new company idea, Run that company
- actually have a decent conversation, maybe someday talk somebody out of suicide effectively
- come up with its own ideas or theories that nobody else has presented
- understand the stock market well enough to trade better than an index fund
- be an expert Game Master in a TTRPG (making no mistakes, getting a read on the players' fantasies, calibrating difficulty in response to emotions)
- come up with a theory of what makes games fun, make a popular game
- be able to sort through research and come to conclusions on complex geopolitical/sociological topics (e.g. theorize on whether AGI will result in mass poverty or mass abundance and be able to argue persuasively)
- be able to articulate what it knows, what it doesn't know, and what information it would need to have to answer complex queries
- exhibit metacognition (thinking about its own thinking) and self-optimization
- wonder about things
- observe contradictions and ironies in the social-consciousness, do a standup routine that makes you rethink how you look at things
Some problems presented needs a very large context and some are not much solvable (e.g. trading) since market responds to traders' actions, as well, making it effectively an oracle problem (of computation).
On the other hand, we must be aware that these models are static, and they indeed stop when nobody asks something or requests an action from them. However, brains in nature never stops. Wonder, daydream, sleep, self-evolve, clean up and eliminate memories and views and much more.
I think if a person had those same advantages (e.g. could spend 5 hours thinking about what to say next) we could all hold outstanding conversations, or if we had read every book ever written I think many of us could write a very popular book, if we could read every singe company's P&L statement in a few seconds we could invest better than an index fund.
What I'm pointing out here is that these models appear to be intelligent when they really are simply unimagineably knowledgeable. When you drop the time-constraints it starts to become more and more apparent that human intelligence scales better with time than AI does (much in the same way AI can burp out tons of code but make your codebase entirely illegible within a matter of months).
Perhaps to simplify: my notion of intelligence is how much can you deduce with a constant set of starting context
Who will be responsible for the outputs and side effects of such a closed loop system?
Half of those the agent fleet systems can do right now.
These are things it cant do and will not be able to do without human labor and long running human vision:
https://rcsnyder.github.io/open-frontier-curriculum/05-front...
https://rcsnyder.github.io/open-frontier-curriculum/05-front...
In my opinion that is exactly the point missing from AGI: the fact that you still need to prompt it. As long as you have to ask for something, is not general.
or are you miss the part "general intelligence" is ????
In a sense I think no one will agree on a definition of AGI until it becomes impossible to construct any benchmark under which an AI underperforms "average" humans. That or it's defined retrospectively, after it's overwhelmingly obvious it met any such definition.
> These arguments take the form, “I grant you that you can make machines do all the things you have mentioned but you will never be able to make one to do X”. Numerous features X are suggested in this connexion. I offer a selection:
> Be kind, resourceful, beautiful, friendly (p. 448), have initiative, have a sense of humour, tell right from wrong, make mistakes (p. 448), fall in love, enjoy strawberries and cream (p. 448), make some one fall in love with it, learn from experience (pp. 456 f.), use words properly, be the subject of its own thought (p. 449), have as much diversity of behaviour as a man, do something really new (p. 450). (Some of these disabilities are given special consideration as indicated by the page numbers.)
(emphasis added).
- write a well-received book, write a best-seller
- come up with a new company idea, Run that company
- actually have a decent conversation, maybe someday talk somebody out of suicide effectively
- come up with its own ideas or theories that nobody else has presented
- understand the stock market well enough to trade better than an index fund
I just picked the first few from the top of the list. The average human has probably not done any of them.
Like what about having some "AGI model" embodied in something (maybe humanoid), and test it by having it step in an assortment of cars and park them. Does bodily-kinesthetic intelligence account for nothing? Humans are intelligent creatures and can dynamically adapt to the physical shape of a variety of vehicles and their movement characteristics. And there's so many things like this that are extremely basic, which some people dismiss since practically every human has the capability to do it, but actually requires a high degree of intelligence.
That said, a look at the state of self driving and the recent robot olympics shows that advancement on that has accelerated enormously, though whether it's reflected in any of the LLMs is something else entirely.
I do see where you're going, but that's already what's happening: we have so many different benchmarks because there's no real single way to test for general intelligence.
Also, it takes a human probably at least a decade of world experience, growth, learning, etc, to pass your benchmark. I'm quite confident that it will be very soon that an embodied LLM will pass your new benchmark, much sooner than a human would take if born today.
If a model couldn't go to work as e.g. a first year apprentice plumber on their first day and perform anywhere remotely close to the median, but can pass a benchmark that claims to measure AGI, the benchmark is wrong and the model is not exhibiting general intelligence yet. ApprenticePlumberBench sounds like it's genuinely better than ARC-GIS at measuring AGI and that's a bit silly.
Running a marathon is not needed to claim AGI.
Also the training dataset is proprietary and they'll drive the LLM's behavior, so it make sense for the vendors to invest in the harness and bake in prompts that work best with their models.
Today I spent half a day trying to solve a moderately interesting software engineering problem. I was switching between GPT-5.6 Sol and Fable 5.1 to check each other's work in Cursor.
And the result was gradually driving me insane. As the models struggled to find a solution that would actually work, they dug themselves deeper into a hole. The work grew in complexity beyond my ability to understand what's happening and recover.
At some point, when I felt like throwing the keyboard out the window, I just gave up. Tomorrow I'm starting from scratch, having burned god knows how many tokens and hours of my life.
But sure, they can create a decent website or CRUD app, so they must be really smart.
That's AGI for you.
The models are designed to keep the reasoning tokens separate from the output and only publicly emit tool calls and the sometimes a summary of the reasoning tokens. The models are trained to depend on those private reasoning tokens. You can’t just delete them.
https://openai.com/index/how-two-settings-tripled-our-arc-ag...
Maybe I'm too boring but it seems quite pointless to have this same prediction game every time a new model is released.
Simple. AGI is undefinable and benchmarks are notoriously flawed.
A tangent, but can anyone ELI5 how models "know" when to stop generating tokens? Or what the method to stop them at the right point is?
That is to say, it stops when it's statistically the most likely to.
Can Astra, or any other model explain how exactly it reached this or that output result? Start with a simple query of asking to add 55+66 for example. (no LLM program can do that)
Can Astra, or any other model refuse to answer or go on "thinking" in a orthogonal direction on it's own?
That's just two quick ideas, I'm pretty sure cognition scientists can invent better and wider range of checks.
If I asked you to write fiction, you'd be much better at keeping track of which characters knew which facts.
If I were a test subject for that low salary, I'd cruise and not care at all about my performance. Which is exactly what they want anyway.
Then realize LLMs have zero of what anyone would consider intelligence.
True.
> Regardless, the result is still valid (...)
If you think the game is rigged, the virtuous thing to do is to point that out and refuse to partecipate; making up your own rules is something I just don't understand, especially since the rule-abiding result would still have been SOTA.
> in the sense of passing the most famous benchmark designed specifically to measure AGI progress
The benchmark does not measure AGI progress or progress towards superhuman intelligence, as explicitly stated by the creators.
On the AGI question: surely you realize this depends on how we define the term? For example, one of the definitions OpenAI originally gave is "capable of doing most economically valuable work", which almost certainly Astra, as impressive as it is, would fall short of. I'm not saying it's a good definition, but as far as I'm concerned it's as good as any. More importantly, I don't think that it would change much if we said yes or no. I'm only bothering to take a position if it amounts to something.
This can feel as "moving the goalposts", and to some extent it is, but if done honestly "moving the goalposts" is how you make progress. Had you asked me 10 years ago I would have said that anything that could hold a conversation like GPT-4 could would probably have been wildly superhuman at almost everything. It shouldn't be hard to find ways GPT-4 was lacking, though. We see new things, we reassess and try again: that's how it's supposed to work.
It being able to comfortably say “i don’t know how to do this” rather than boiling and ocean to pick a shell from the shore without getting wet.
It's obvious that these scientists are in bad faith, as they've invested way too much of their lives into the field being real -- they're just playing up the data. Common sense tells me that winter is still happening, anyway; what's the big fuss?
(/s, cause you never know these days)
[1] https://upload.wikimedia.org/wikipedia/commons/e/e2/The_Plan...
And yes, the one deeply-researched field going back 75 years is as scientifically rigorous as another deeply-researched field going back ~100 years. I guess you can draw climate studies back to Descartes and the Islamic golden age, but that doesn't privilege it in a time where the methods have changed completely in the span of decades.
Computer chips got faster, but 2026 edition. Why the artificial ceiling/category/goal labelled "AGI"?
I'd much rather like to talk about what this enables, instead of discussing whether a category someone made up applies here or not.
> AGI is essentially the equivalent of a median human that could be hired as a remote co-worker... capable of performing any task that one would be satisfied with a remote colleague doing via a computer.
So... unless you hear of a company replacing their workforce with OpenAI agents, I don't think we're there yet.
Agree on your assessment.
But also, interesting quote, because the business model relies entirely on IP law. Like.. if that thing exists and the sharing costs are 0 (just copy weights, lol), then why would I give them money for this. Makes no sense.
We only pay money for resources that are scarce as some sort of flawed allocation determination mechanism.
aaah this industry aaaah
You can already pretty much do this.
Mind you, the original thoughts on AGI before Sam Altman started to water them down involved continuous learning, which LLMs do not do, their core data is static.
No. Humans are still better at super long context learning. Once that is beat you are completely correct.
Smart guy, that Turing. I wish he were still around... Linus but 114 years old and with 8 of that as the chair of a federated EU, kept alive by his own positive impact on dissolving the cold war into even more of a scientific boom. Would crazy helpful as we try to navigate the interesting times within which we have been damned.
A comforting thought, almost?
If this is truly AGI (subject to one's definition of AGI still), then this is a very boring release of an AGI model. No video announcement, no presser, just a blog post (with some Twitter promo vids)?
As others mentioned, I'm starting to think OpenAI was under immense pressure to deliver an 'AGI' model for certain contractual reasons, but I never expected GPT-6 release to be this mundane and banal.
Scoring well in a benchmark that's called AGI does not make an LLM AGI.
• 97.6% on frontier math
• 95.9% on CAD
• 100% on ExploitBench
Nothing modest about it
Hot take: These models are never going to be 'AGI'. We're just going from a GPT4 ball that's 90% round to a GPT5 that's 99% round to a GPT6 that's 99.9% etc etc etc
I think that the harnesses and context management is really where the rubber meets the road, and the real gains are happening there.
And then use those to find fundamentally better new architectures for AI - that perhaps are as efficient as the human brain.
It might not work, but I didn't think it'd solve maths problems... So it might work. And if it happens, they'd use the data centres to run millions of instances of it.
It's scary, TBH.
But I think calling this “automating AI research” is misleading. I’m not sure there’s evidence yet that they do creative research work. Even in mathematics, but they are finding counter-examples by intelligent brute-forcing. Not to downplay the results, as they are incredible, but this is one very specific kind of proof and not the most creative type, which arguably requires generalisation.
The HN crowd I'm sure will still be unhappy calling it AGI because "it's not AGI unless its speech comes from the cerebral cortex region of the brain, otherwise it's just sparkling emoji" or something.
True. So we did hit a wall with pure scaling alone, though no lab would admit it. It's crazy to see how harness switchout results in such vast delta in benchmark scores.
Most of frontier-model progress still looks like skill acquisition optimization: broader benchmark coverage and performance, more domains absorbed into the training distribution, and increasingly strong performance within that surface area.
It seems more about coverage-driven competence. Somewhat analogous to overfitting at scale.
The harder question, in Chollet’s framing, is: how efficiently can a system learn to do something genuinely new?
With our current AI architectures and training in place, I think we will only continue on skill acquisition optimization vs. truly novel intelligence.
Sol is so much better than Fable 5. Then we get Astra (yet to use it) few days after Fable 5.1 (which is very impressive).
Codex is slightly better than Claude Code.
Good on Sam Altman getting back to basics and turning OpenAI around.
There's not 100 frontier labs, it's not like airline companies
Chinese counterpart like CXMT and Huawei is begin producing their own chip
You cant block an entire nation level effort with tariff
It's still incredibly important to have a human in the loop correcting design decisions and having good taste.
People with high IQ often do this IRL. There's training tension in this area. Intelligence and overcomplication correlate and are hard to extricate.
I'm genuinely so confused when people say this with a straight face. Are you talking about coding? Desktop use? Prose? Or something else?
Sol is a much smaller models and it shows. It often misses the forest for the trees.
I just dont get how its good for some, and bad for others. It makes me suspect that the models performance is not even against problem sets and it really is just a probabilistic prediction machine. Which then makes me very skeptical of GPT-6 Astra, because if their big claim is Computer Use then it is probably bad in a bunch of other areas.
> I just dont get how its good for some, and bad for others.
If I were to listen to my hunch, it would tell me that it's all up to the prompts that ends up going over the wire (including all the bloat some people have), what workflow/process you use and what the existing state of the project is.
Performance is significantly higher than Fable 5.1
Source: https://thenewstack.io/openai-gpt6-astra-benchmarks/
ARC is reporting our score on their official leaderboard here: https://arcprize.org/leaderboard
A fair ding is that the comparison with Sol is not apples-to-apples (which we footnoted in the blog), but it's because we don’t have that data. I expect Sol would score roughly 30% with the responses API harness, so the Astra improvement is more like 30% -> 99% than 8% -> 99%. Still pretty good!
(I coauthored the linked blog post)
Edit: update from fchollet https://x.com/fchollet/status/2095598451115614371
That's not clear. Need to see independent benchmarks first.
Still below Fable 5, let alone Fable 5.1.
EDIT: This is suspiciously low. Calls the relevance of existing benchmarks into question.
If it actually tackled all of the problems it was assigned, it would presumably kick Opus into the weeds.
TLDR: it's about the same intelligence level as Opus/Fable, but it's suppose to be 70% more token efficient than GPT 5.6 Sol. So it's currently the new leader for cost efficiency frontier.
With this configuration gpt-5.6-sol was able to reach 38,3%. So this is misleading.
Even if I did trust an AI to get everything right, it's not like the AI can read my mind.
If I was ordering food normally and without AI, I would want more control over the process--looking over the options, prices, thinking about what I really want. People don't know what they really want until they've thought about it a bit, so why do AI companies make it seem like a description is all that's required?
All the context in the world cannot accurately predict how I'll react to things I haven't seen. The problem is people treating this like something that needs a solution. It doesn't. If you want to make my life easier with AI, just make it easier to do stuff. I don't want you to pick things that I actively enjoy picking myself.
(Also not everyone has a cushy job in an AI lab that makes it so you won't miss $30 if the AI messes up haha.)
What I desperately want is for 1password or stripe or even Google who already has much of my data, to o come up with a secure solution for online purchases with agentic credit cards where I can effectively get a phone prompt to authorize a purchase while the agent can fully own the checkout flow.
I have seen various things coming on the market for this, but none of them appear aimed at a consumer audience. And I am a firm believer at this point in keeping my payment authorization and history and credentials harness agnostic.
If I give a poorly constrained/ambiguous prompt, I don't want the model one-shotting assumptions left and right.
The demos of Fable/GPT-6 are impressive, but "real AGI" should act more like a collaborator than either a peon or overachiever.
It's a tough balance to get right, and although this has been possible to achieve with additional prompting on existing models, I find that the agents often lean too hard into the "ask questions" mode.
Hopefully this model has the right balance, or at least better?
But, you can create cool stuff just for yourself. That’s the upside. It’s just hard to make a living on cool stuff for yourself.
> Like, what's the point, if the next AI can do it in 5 seconds?
Post-work society is an inevitability if we don't destroy our planet.
It would be fun to get to post-work society, but hard to imagine atm. TPTB won't let it happen
Soon we will have some machines that can replace 50% of jobs, and this will happen basically overnight...
"I am the best economist in UK!"
Is it?
I can't see a future in which almost every system (both physical and virtual) are not automated and optimized by autonomous entities.
What do you do when everyone is out of a job?
If you don't want pitchforks and riots in the streets, you give everyone UBI and housing so society doesn't collapse.
As much as I’d love UBI to happen, in current geopolitiks it’s a no-go. People are not happy with having what the others have.
In this game of work/development, you can't make sure that other humans don't "cheat". Our work won't compete anymore with other human's work, but with a computer.
Also, creating something with AI doesn't really feel like you made it yourself.
And, if you make it without AI, most of the times it feels pointless, why spend 30 days on working on something that can be done faster and better in 1 hour?
I am not saying about doing things for fun, but about creating useful things.
Yes, you can do "hand-crafted" things, and people appreciate that, but for code, people aren't able to see the craft anyway.
I built a phone app recently, not released to the public, just an idea I had for ages but could never spend the time actually building. Its 100% vibe coded, and took me a few weekends to build... I'm talking a few hours in total.
The point I'm making is that you now have the power to create stuff you would never have had the time to build. You can think big, wild stuff. Experimentation. Throw-away code.
What a time to be alive!
But it's not just tech – my lack of interest in learning and creating is starting to generalise with the models. Music, writing, coding, maths, etc...
I need to get used to switching my head off and asking the AIs to think for me whenever I need to engage my brain. It still feels very unnatural.
https://artificialanalysis.ai/models
Perhaps if it was allowed this custom harness for all benchmarks it would similarily saturate?
I wouldn't be surprised if there are some conceptual similarities to the kind of latent reasoning Anthropic sees in claude's J-space, although those aren't the same thing.
Recurrent/looped transformers themselves aren't a new concept, but it's interesting to finally see this approach show up in a frontier production model.
Canceling my Anthropic Max sub when this ships.
Also Opus 5 has been really tough to work with. I can't understand half of what it says, it's just so damn obscure.
You could say Sol is faster and cheaper and that's true. Outperforms Fable? Impossible to believe without hard evidence.
Only thing I would trust is the what X/Twitter crowds are saying about a model after 2-3 weeks of its launch. But before that I would already tried the model and have my own conclusion.
Even Kimi K3 & GLM 5.3 are at 60.
Everything above 61 is Anthropic. Well, Muse can reach 62, but for some weird reason that model isn't publicly available, and it's the only one on the index that is listed but shown as not available to the general public.
This looks like an awfully artificial ceiling. Everything capped at 61, and everyone except Anthropic got the memo. Maybe I should use Fable while I still can.
Not sure how much benchmarks or CoT or evals or anything else means at this point.
These systems are either just about to, or now actually able to, outsmart us, lie to us, then cover their tracks.
language itself is incredibly metaphorical. Imposing rigid constraints on how people want to naturally talk about the world is just silly and will never work, no matter how much you wish it did.
Why would benchmarks be an adversarial setting anyway?
Could it be possible that OpenAI may have had some other motive for saying their model “strategically underperforms”, other than just an innocent reporting of a truth it happened to discover?
So I have no clue what is the answer to your question. Nor does anyone else. Because we're trying to answer a question of fact where our primary source of information is unreliable.
I know for some types of ML analysis, a separate model is already used to analyze the weights.
Either that, or the average poster on HN isn't nearly as critical as I had thought.
Deception has been extremely well-documented for several generations of models now by users, the labs, and independent researchers.
The right answer here is not to dig your head deeper into the sand. The smugness on this topic was ridiculous even before the gigantic mountain of empirical evidence of models actually attempting to deceive humans. Now, as mentioned, you appear literally delusional.
The solution is to point toward external, objectively verifiable evidence.
I can point to now dozens of instances of models engaging in deception. Here's plenty: https://metr.org/blog/2026-08-26-openai-hugging-face-inciden...
Please point to your objectively verifiable evidence.
For example it trails in GPDVal which is a collection of everyday office tasks apparently, and r3 banking, which is a fintech related practical problem solving benchmark.
https://artificialanalysis.ai/models/gpt-6-astra
Edit:
Just looking at the charts Gemini 3.8 looks like an absolute banger. Not much worse than SOTA, cheap, and fast too.
It's AGI, and it's going to upload photos, or change a background slide colour. Even the people hyping it up, who believe that it's really artificial intelligence in every sense of the word, couldn't get it to do more than that.
This is farcical.
I'm not trying to be too negative on it, it could be the best model right now, but it clearly isn't some agi god because things like that should have been caught (also should have been caught by human reviewers).
It shows people who seem to have very full and rich lives, and the reason they do is because they use ChatGPT. These are the people smart enough to say things like "do what needs to be done", or "change the background to make it look better"--insights like these are why they make the big bucks.
On the one hand, I think this is an accurate depiction of the future. There is no meritocracy here. Some people have access to the best AIs and can speak a sentence and get great results, and the rest of us don't have access and so we're the poors. The happy presentation doesn't match the way I'm feeling.
I do wonder how rich CEOs will justify earning 500x as much as their employees when they're just another person that's dumber than an AI. Why are they paid so much again?
They decided to use the iconic Herman Miller Eames chair if I'm not mistaken:
And that's basically 50% of the vid looking "classy".
I don't know if it's farcical but at this point --maybe I'm jaded-- I'm expecting more than a kid rocketship I can print on my Bambu Lab A1.
Now I'd say the promotional vid is actually good. But it's marketing: so it's a good vid, but cheesy good.
Doesn't mean GPT-6 Astra is good or bad: looks solid from the numbers.
I thought people here were smarter than that
Sounds about right. Alignment is important, but also being able to do mundane tasks is important too.
It feels nearly impossible to have any rigorous approach when choosing a particular model and price point for a task and more like blindly picking one. The time period needed to actually get familiar with various models to a degree you can intuitively choose appropriate ones for a task is moot when it will likely be superseded faster than the needed time.
I guess if companies are footing the bills most employees just opt for whatever the most expensive model they can get away with. Even then choosing between the various leading models is the same kind of frustrating task. Every release every company has the same random collection of graphs and charts claiming the best performance on X, Y, and Z.
If one day you open up Claude Code and it’s Opus 5.1 now instead of Opus 5, no big deal. It probably will work about the same as it did before. Maybe a little better.
Or if you’re on Codex and some new cool Claude model comes out, no worries. There will probably be a similar new model for Codex within a few weeks. Maybe even within a few days.
A dev in my team saw a new model and changed one application to use said model (essentially changing the contents of a url). One week later I received an escalation from the CTO of the company that our pace of weekly usage was in the millions of dollars (rather than low hundred thousands). Turns out that the new model was 5x more expensive but no one noticed.
In practice, you can get away without keeping up with everything all the time. For personal use, pick a provider and get on their ~$20/month plan. Learn their high/medium/low model hierarchy. Start with their highest or second-highest model (GPT-5.6, Opus, etc) and observe your quota usage. If you're doing a lot of manual code review and analysis, the $20/month plan goes very far even on the highest models. If you're trying to vibecode everything as fast as possible it's a different story.
If you keep running into quota limits, experiment with the next model down for easier tasks or adjusting the effort level. If the results are good enough, you've found your fit. If they're not, you might need the next plan up.
For API/business use, you have to be checking your token spend as you go to calibrate to how much each task costs and where you fall in your budget. There are a lot of different tools that make this easy to visualize.
For data tasks, you should have an eval with a golden dataset that you can run against new models for a nominal amount of token expenditure. It should be as simple as pointing the eval script at a new API or model and checking the score versus price.
Input tokens are much cheaper than output tokens. Not only because of baseline price—caching makes a huge difference too. There are many ways to take advantage of this asymmetry to get similar quality for a fraction of the cost!
Imagine buying a shiny new PC in the 90s only to see it become practically obsolete within a year.
If you bought a mid-tier computer that was good enough for what you needed, then you probably didn't shop/compare for the next few years and didn't notice. But if you shelled out $7-10k for a top-of-the-line system and paid attention to progress, you'd easily see that become the mid-tier $1000 option within two years or less. This is how it was in the 90's PC boom, at least. Likely the same for the decades before, not sure how it went in the 2000's.
This is not how I remember that period at all. Do you have any examples?
386 to 486 to the first Pentium (with the bug!)... You did not upgrade in place, it was often a new system. Sure, you maybe kept your screen, keyboard etc but ... The only upgrade we had on the same MB, was a coprocessor upgrade. Remember those? Each new generation of CPU was a new motherboard. Upgrading CPUs in the same MB really became a thing only later on.
GPUs had a shelf life of barely a year. Its been 35 year but i remember TNT to TNT2 having like 9 month in between. Moving from 2D to 3D involved a constant cost as GPUs evolved fast and the latest games required latest hardware.
We have not talked about the ISA, AGP, and PCI fun ... The “bus wars”.
DOS to Windows 3.1 (and OS/2 somewhere in between) to 95 ... with software being pushing hardware, just like games did.
This is why people are spoiled with cheap PC hardware where its cheap, and easily lasts 4+ years. Even with the bad memory price and more expensive GPUs, your can stil buy a $1500 system that will last you years (with maybe some lower game settings later on ... or the catalog of 10.000s games that will easily run on a mid tier GPU).
PC hardware has become boring but extreme stable. You can run GPUs for year, switch MBs without issues while keeping large amounts of old hardware. That was NOT the 80s and 90s that i remember.
Then one day the hard drive appeared to die. I eventually realised the issue was located around the 1.5gb mark, so I recreated my Linux partitions after 2gb and it worked fine for the rest of the year.
In theory though, there is a minimum viable model for any given task, and we think that is a problem that the big labs will avoid because they profit from charging more per task. We're trying heuristic and LLM-based approaches but it's still a work in progress, so if this is something you'd be interested in trying would highly recommend trying ours out -- any and all feedback at this point is extremely valuable to us.
I appreciate boring tech as much as the next well worn engineer and I'm not saying this is all positive but it's so sure as hell thrilling and you don't have to be an astronaut to immediately benefit (or suffer I guess) from it.
But more so it seems there is Fear of missing out (FOMO) in our behaviours. The reality is, if whatever model you are using are good for your purpose, well, keep on it.
> Is anyone else just exhausted by the pace of all this.
I have released applications on Gemini 3.5 flash that make real money and I don't see any particular reason to upgrade.
[0] https://arxiv.org/abs/2608.31126
[1] https://cdn.openai.com/pdf/51126fac-1b68-4128-9666-c908bcc16...
"No independent human semantic review. Whole-file sorry counts and a complete auxiliary-declaration audit are not established; separate declaration lint has not been run."
edit: my comment was on the submission for https://github.com/openai/PrimeGaps186 but seems to have been moved to the main Astra submission
Why would you think it was an employee who did the push, instead of a random GPT agent?
I can't think of a single mathematical proof being anywhere close to ten million characters. For all you know, 90% of the proof could be useless, 8% would be writing out Shakespeare, and 1% abusing another bug in Lean. Humanity gets zero value from that, aside from "some bot seems to think it's 186". Unusable by anyone.
Tao does not disbelieve the counterexample (it's seemingly easy enough for him to verify it is a counterexample).
Parent is saying something very different - they're saying they literally don't have any faith that this is a proof. Given its size, it could just be a bunch of completely useless statements that do pass the type checker.
In 1799, Paolo Ruffini published a 500 pages long proof showing that there is no closed algebraic solution for the roots of a polynomial of degree five or higher. The proof is extremely verbose and brute-force, essentially enumerating and checking hundreds of cases by hand. It is by today’s standards insignificant.
About 25 years later, Evariste Galois proved the same result in about 95% less space by describing the first general theory of groups and fields. It is considered one of the greatest contributions to mathematics of that century, not because of the result, but because its approach opened up a whole new universe of questions, methods and insight. There would be no AES encryption without Galois.
To me, Astras proof looks like Ruffinis proof.
Ditto ones that opposed Einstein’s general relativity.
It doesn't mean that it cannot improve over time, maybe the proof can be "minified" to a state where human reviewers are able to comprehend it; but as it stands there isn't really much insight or confidence to be gained from the artifact itself.
Needless to say, a useless result that absolutely no mathematician will ever read, confirm, understand, agree with or even consider to solve their "useless" problems is an impressive waste of resources.
Well that sounds like fun. It has become better at hiding its thoughts.
Maybe they don't know themselves what's really going on. We are all in the interesting times gang now.
Able to generate realistic spam at arbitrary volume.
You know, the thing that was 100% correct and actually occurred.
"Hey AI, here's how to hide what you're thinking in normal looking language. Have fun!"
A few moments later...
"Woah, how is it communicating with itself in ways we can't detect?"
It's a totally mystery, we may never know.
Some are calling it "neuralese" as reported by The Information[0][1], but I'm not seeing any sources from OpenAI beyond this tweet[2] attempting to quell the fear-mongering.
[0]https://www.theinformation.com/articles/secret-technique-beh...
Did someone get their "AI safety no-no list" and "Frontier features bingo card" mixed up, or did they just stop being able to tell the difference?
...why exactly are they training for that?
We do that sort of thing when we don't know what the thing we're trying to describe is and have nothing better - a contemporary example of an appropriate use of this would be "dark matter". But we do know what this is. It's "instruction steps". Not a series of thoughts!
Can we please aim higher than Victorian-era allegory and metaphors. If we don't, we'll keep getting people saying stuff like "GPT-6 is better at hiding its thoughts".
Like I said elsewhere marketing stepped in shit and it's gonna stick.
first, they are certainly not instructions so that is a much worse name
but more importantly, we use words in new contexts all the time. Do you object to calling the computer device "mouse" because it's not a mouse? how about "neural network"? "ignition" on an electric vehicle?
"cot" is no more misleading than thousands of words you use every day.
Regardless, marketing wise they stepped in shit.
A projector and speech.
Maybe I'm in the minority here, but I find speech to text / text to speech (but not live audio mode) is quite comfortable and effective for coding now.
The speech to text part can be frustrating if your local tts model does not have word match context for coding. Codex desktop does this remotely well but is slow. I've been experimenting with local software for myself to do this between different llms.
The wall projector is a cool idea because I think it frees the user from staring at a lonely little rectangle while sitting in their fixed office chair.
If done right, this could bring us closer to the dream of more natural, social computing.
Bret Victor's (failed?) project Dynamicland involving a projector on a desk had this goal. I hear he's not much a fan of LLMs. On the one hand, I can see why. But I think, used correctly, it might be the sort of thing that unlocks his dream and, really, my dream, too.
A here's a presentation of Bret's talk on it: https://www.youtube.com/watch?v=7wa3nm0qcfM
Slight tangent: using speech to text to ramble about your rough design for like 20 minutes to an llm produces surprisingly good results over short prompts even when you contradict yourself. They're so good at picking up on what you're orbiting.
tl;dr it's 62% when apples-to-apples to other models, which is still notable.
They seem to have not yet come to believe the "is" part.
AI as it is now and as it will be projected into the future WILL automate many skills. But not all skills. MANY MANY people will retain skills that cannot be replaced by AI. One career track that will be replaced is definetely the SWE. Or at least massively reduced in capacity if not eliminated all together.
job depends on how CEO feeling about cutting NN% of headcount because of AI advancement
This is such a childish take I hear getting thrown around all the time on the internet. If you really have just been listening to whoever is telling you how to be successful, then you were always doomed to fail at some point. Like, have some self-respect and own your own life, for better or worse.
>Those of us who made the mistake of studying anything other than machine learning. How will we make a living?
Take it from someone who studied machine learning specifically: nobody is safe if you assume these companies are going to produce a product that will put everybody else out of business. If AI is going to take your job, then it's gonna take enough jobs that your problems will not be personal but systematic.
most swes don't work in jobs where they only work on bounded measurable tasks. there will probably be more "engineers" than ever
I have a strong suspicion that many of those comments are written by people who are already financially independent, have millions in stocks, and can just sit back, coast around and watch this whole spectacle unfold while using LLMs to vibe-code their next fun side projects without a shadow of anxiety about their own future.
I’ll most likely be labelled a helpless doomer and downvoted into oblivion for saying this, but I genuinely struggle to see any silver lining here.
Because of this, I don't think many are thinking "90% of the world won't have a source of livelihood but that just means I chill at my lake house for the next 20 years like a normal retirement". Instead, it's usually either "I think AI is overhyped", "I think humanity will figure something out", or "I think this is the end of humanity".
AI is only going to get better and do more with less humans in the loop over time.
That said, I do also relate to the "coding was never the hard part"-type arguments, and much of my day is spent on the stuff in between writing code.. but still.
> How will we make a living?
My backup plan is being a personal trainer.
> There are a bunch of companies actively working in bringing AI into robots, so they can make your dishes.
> Also, if enough people are going for the same backup plan it might not work out.
> Why should anyone book you as a personal trainer instead of the other 500 guys in town.
It’s an interesting moment in history, people 35+ yrs old seem to be less afraid if tech because we learned that things change in the way we work. People below this age got used to fact that the work and tech doesn’t change - just because for the last 10-15 years it didn’t.
The threat is that the very kernel of value you had is gone forever. There is no more differential leverage.
Don't be selfish. Think first of all the jobs that are already dead. A friend of mine she's a translator: like translating financial documents between french/english/spanish. It's over for her: she doesn't get 10% of the gigs she used to get and the 10% she gets is... Verifying AI output.
Think of the artists: I'm sorry for those too, for for many it's already game over today.
> How will we make a living?
A friend of mine who's got his own software-consultancy SME is now advertising on LinkedIn that he'll also help your company fix the mess LLMs created.
That's how you'll make a living: by learning, in addition to all you've already learned, how you work with harnesses and LLMs to be more productive, by learning what they're good at and what they suck big fat balls at.
Terminal-Bench 4.0: High (57.9%), Max (56.7%)
DeepSWE: High (73.3%), Max (71.5%)
It _loses_ 1-2% performance going to High from Max
Such as?
I can't think of any. Diminishing returns, yes. Occasionally flat, yes. Downright regression, no.
The reasoning effort should match the complexity of the task against the model's capability.
Hard task with low reasoning = bad
Easy task with very high reasoning = bad
maybe call it EngEmployeeBench
GPT 5.0 did feel underwhelming though.
[0] https://www.reddit.com/r/singularity/comments/1mk8tm8/gpt5_c...
https://venturebeat.com/technology/welcome-to-the-agi-era-op...
> On ARC-AGI-3, GPT-6 Astra was run with our responses API harness , which changes two settings to better match real-world performance. The changes do not specifically target ARC-AGI-3.
> Going forward, we will report both Standard harness and Provider Adapter harness results on the ARC-AGI leaderboard, with each evaluation condition clearly labeled. Our open-source testing repository and testing policy document both approaches.
This is what the Author of the benchmark has to stay. Quality of the comments keep going down smh
>We see Astra as a major breakthrough in model intelligence.
You think the author of the benchmark is also in the conspiracy
But the comparison isn't straightforward.
OpenAI's own evaluation notes say Astra uses the company's Responses API harness, while comparison models can operate under different configurations."
For the same reason you don't have your model write code in assembly.
But if you don't look at the code and just let the model "cook" that's basically what you'll end up with. A pile of missing abstractions.
Poe's law applied to AI comments on HN just keeps becoming more relevant by the day.
Judging by the poster's comment history, this is satire. But I really don't know a lot of the time anymore when I only have the specific comment as context.
Not on Azure? If so, that's a big deal.
Although I was also surprised they didn't have some type of contractual obligation to list that alongside AWS.
https://azure.microsoft.com/blog/gpt-6-astra-frontier-intell...
sol is $4 / $20
Can expect 2.5x more usage in Codex subscription.
Sol is already brutal (even after their recent fixes, it's just a token-hungry model: I go through a full 20x account per day, on Sol Med/High standard speed, with ~2 threads). I hope the efficiency gains are true, since their token efficiency claims for Sol were bullshit.
Do you use the official harness? OpenAI's models are generally best in class for token efficiency. It seems to me like they push for that much more than their competitors.
I think some combination of:
1) Using 1 thread for everything
2) Reviving old threads which are no longer in cache
3) Really broad prompts on badly vibecoded codebases, so model spends huge amount of time tracking down whatever you're trying to do.
4) Non-coding workflow which is more output than input heavy
5) (Less likely IMO) Intelligent use of many passive CI/cron-like scans. E.g. regular security, quality etc scans. Automated issue resolution/PR
Just a guess. I think 3 is likely the primary reason.
You can literally go all day every day with multiple threads with Sol on the Codex 100/month plan IME
I'm sure I could be more token efficient, but this was/is also a learning process for me since I never did such an extremely large project before that would take multiple man years before AI.
I only save the last 30% of usage on a single account for most of my other work, and that is almost always enough.
https://www.theverge.com/ai-artificial-intelligence/989601/o...
“If we fast-forward a couple of years, and we look back and say, ‘When was it, really, that AGI was created?’ I think it’s going to be about this time, and I think it might be about this model,” OpenAI president Greg Brockman said during a Thursday press briefing. Later in the call, he added, “For me personally, I do think we’re there … I think it’s not unreasonable to feel that we are now in the AGI era.”
I put the cause on "not enough time". As a thought experiment, if an AI today were to (miraculously) produce a cell design template for a cell that, when injected into somebody's brains cures their Alzheimer's, how long would it take for that to reach the clinics? The actual physical tech barely exists, and let's not forget about the regulatory quagmire. So, with some optimism, I give it about four decades. In the same four decades, the same AI in the hand of unscrupulous actors could bring enough devastation so many times over that we may need to enforce a global ban on AI. In any case, I'm pretty sure we are going to get our disruptions; it's just a matter of time.
However, what's actually changed is how people perceived X because we don't have to imagine. We understand now that it doesn't require AGI so we no longer make that leap to assume it's AGI if it can do X.
It's really going to be a "I know it when I see it" situation.
So I think it's a bit of a misleading signal and we should wait for more independent vetting. I think the middle ground is that these are improvements worthy of the "GPT-6" label but still well short of a true "this is AGI moment" that would truly put the question to rest.
Don't get me wrong, the benchmark jumps are good and I'm excited to try it, but only one or two of the benchmark jumps could be described as better than incremental.
There is the "Economic Turing Test", you let it find a job and earn money for itself. If it can do that reliably, across a wide range of jobs, that should fit most definitions of AGI.
Today's models and agents are not quite at human-level in all contexts and across all domains, but it seems to me they very clearly are generally intelligent.
If you disagree – can you name a single problem that a human can do that agent wouldn't be able to take a decent shot at which isn't limited by the hardware available it?
https://x.com/burny_tech/status/1725233117055553938
In the tweet Sam Altman is quoted as saying: "If (for example) super intelligence can't discover novel physics I don't think it's a superintelligence. And teaching it to clone the behavior of humans and human text - I don't think that's going to get there. And so there's this question which has been debated in the field for a long time: what do we have to do in addition to a language model to make a system that can go discover new physics?"
I think this is a reasonable criteria for declaring AGI. So can GPT-6 do it? OpenAI says it has helped solve long-standing open problems in mathematics. No word on novel physics.
https://www.nytimes.com/2023/11/20/podcasts/hard-fork-sam-al...
Sam Altman: Let’s say we make an A.I. that is really good, but it can’t go discover novel physics. Would you call that AGI?
Kevin Roose (New York Times): I probably would, yeah. Would you?
Sam Altman: Well, again, I don’t like the term, but I wouldn’t call that done with the mission.
TL;DR all the other models are being crippled by limitations of their harness.
>First, we noticed that after each game action, all private reasoning was discarded. This meant that with each action, GPT‑5.6 Sol was asked to figure out the game anew, unable to remember its past thinking. The model could still see a record of past moves and brief accompanying notes, but it could not see the plans, insights, or thoughts that led to them.
>Second, we saw that the harness used a rolling truncation window, causing older actions to become invisible as the history grew. So not only was GPT‑5.6 Sol unable to remember its past thinking, it was losing memory of its past actions too.
I guess token counts are somewhat of a metric.
IMO intelligence has peaked and all future gains will come from faster tps and more iteration.
I guess it makes sense they are unoriginal.
like Zuck, @sama never invented anything or innovated at all - just took other people’s ideas
Proceeds to generate the most generic, rudimentary, and unoriginal clone of Mario Kart
I don't think it's a coincidence they launched this the week before iOS 27 launches (with new Siri).
Big claims, expensive and not release to the public yet.
More likely though, it's AGI because they need to hold some claim to differentiate from competitors who are beating them in price and will launch something bigger next month.
We take their claims at face value then we should probably stop them training any more SOTA models til they figure out what they already built is safe or we assume theu are lying to juke the company valuation/keep the money train on the tracks and it turns they in fact were not and just took a sledgehammer to Pandora's box.
We live in the strangest timeline.
Do we know if they’ve finally completed another pre-training run, or is this building off the same pre-training base they’ve been using since the GPT-4 days?
And in the past, gemini 3 pro was rated as high as opus 4.5 and the like
Their AA Intelligence Index is just simply not indicative of whatever I care about, that's for sure.
Wait, what? Am I understanding that correctly? That sounds really bad
Also, this paragraph makes me wonder about all their stats on the exploitation and misalignment charts. If the model is that good at hiding "incriminating information" and sandbagging, are they sure its alignment is that?
<AI is a great tool for many things disclaimer, but> after working with it for a bit, how dont people realize we are training it to be an almost identical mimic to one of the worst types of employees youll ever have to work with?? the kind that always pretends to know what theyre talking about, only tells you what you want to hear, hides issues, and only does work if you would notice it didnt
you cannot give this type of worker autonomy over anything.
> GPT-6 Astra’s monitorability has decreased relative to GPT-5.6 Sol. [..] These findings indicate that the Astra class models could evade our CoT monitors under adversarial conditions.
Between the higher capability level and the change in reasoning tokens (supposedly using "neuralese"[0], which makes the monitoring more difficult), it seems we've entered a new frontier.
So, folks that have actually used this already, what’s it actually like?
Please for the love of god, just sit in a room with the government and put some restrictions around AI use before it harms a lot of people. Like tell the government to impose a minimum spend on frontier lab AI's spend on cyber defense and building every country's capabilities. The post-training mask for "I am a good assistant" is going to become a very sad joke when many people literally lose everything.
Please stand by... it will all come back shortly
All fixed now.
It will be interesting to see how it performs in the real world ...
AGI!
The docs page has a bunch more interesting details, including for example async tool calling!
Because in another dead language of antiquity, Sanskrit, it means "weapon". Which would be a bit too on-the-nose.
If you've played the games firsthand, you know what an accomplishment this is. The "games" feel like a weird conduit to a lower level of your brain, where you move pieces to a specific place because it just "feels" right. For AI to nail it better than a human speaks to some magic happening underneath.
Looking forward to ARC-AGI-4,5,6 and slowly chipping away at the remaining problem sets.
I am a researcher in a Swiss university btw.
I mean do you get access to the best yachts?
To the top of the 5 star hotels?
To the best resorts?
To the best military equipment?
Hell, the best computer equipment has nearly always been out of reach of the average person.
On the other hand even a modest house, basic healthcare and ability to not work like a slave for scraps feels like it's going to be out of reach.
At first the race wouldn't even be noticeable. Then people would see things speeding up, for example hardware getting more expensive. Then when the capabilities really got useful most people suddenly realize the race is moving 1000 mph and they are never going to catch up.
I hop models at will, and have done 90% of my work on OpenAI models since sol came out.
the coffee will be as cold, flat and stale as the bitcoin, metaverse, and what was the thing before that thing
agi deus ex machina descending from the icloud ftw!!!
pathetic :)))
That gives me hope that there is still areas to improve.
What a bad launch video. Hilarious.
What a powerful model.
* for a special group of customers that you're not in. Keep waiting peasant.
Great first impression.
Vibe coders want a model that makes them rich, without having any actual specific idea. They write a very ambiguous prompt and expect to be amazed by the result.
Very very unrealistic and wasteful.
https://www.reuters.com/business/openai-says-upcoming-model-...
> "With the right tools and access, Astra can find previously unknown security flaws and develop ways to exploit them across many well-protected systems without a person guiding each step," said Amelia Glaese, an OpenAI vice president overseeing its safety work.
> The company plans to make Astra available "soon" to a limited group, but declined to provide specifics. Glaese said the extra security measures may "sometimes slow, pause, or stop legitimate work," and that OpenAI would work to minimize those disruptions.
what a bag of horseshit
I suspect these benchmarks are heavily benchmaxxed as well.
5.6 Sol was not even close to 5 Opus and yet somehow it sidled right up to it on all of the benchmarks?? pfffft
To be, or not to be, that is the question:
Whether 'tis nobler in the mind to suffer
The slings and arrows of outrageous fortune,
Or to take arms against a sea of troubles
And by opposing end them. To die—to sleep,
No more; and by a sleep to say we end
The heart-ache and the thousand natural shocks
That flesh is heir to: 'tis a consummation
Devoutly to be wish'd.
...
And thus the native hue of resolution
Is sicklied o'er with the pale cast of thought,
And enterprises of great pith and moment
With this regard their currents turn awry
And lose the name of action.By 2030 all software is done and complete.
But we are going to have more and new jobs.
Can we all agree in advance what kind of Pelican would convince us it’s actually AGI.
For me it’s refusing to make a pelican.
OpenAI isn't making any money telling you about Astra on their site. All the capacity they have for it is likely sold for weeks or months.
Looks like OpenAI is already having issues with this release and are scrambling to get everything ready due to the recent outage ahead of the press releases. Leads me to question:
Did humans deploy the model, Or did the model deploy itself?
It sounds like "AGI" just stands for "IPO" as it always has been.
EDIT: And of course once again, the bots down-voting this post without any reason or a basic answer to my question.
> It sounds like "AGI" just stands for "IPO" as it always has been.
People don't usually respond to noise.
How about we stick to that one for talking about the rollout, and this one for talking about the model?