Discussion summary
Discussions around GPT-6 Astra include website performance fixes, API documentation updates, and user opinions on benchmarks and AI capabilities.
What the discussion says
- Some users appreciate improvements like website responsiveness and API details.
- Others express disinterest or frustration with ongoing debates about AGI and benchmarks.
- A few mention the potential of GPT-6 Astra in practical applications and marketing.
“The docs page has a bunch more interesting details, including async tool calling!”
“Huge gains on some benchmarks, but for coding it sits barely above Fable.”
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- Hacker News
- Related: OpenAI begins rolling out GPT-6 Astra - https://news.ycombinator.com/item?id=49554273
How about we stick to that one for talking about the rollout, and this one for talking about the model?
by dang - I feel that for some time now, the biggest constraint when working with models is not their intelligence, but their speed. It does not matter how smart the model is, it will make mistakes, because the instructions are ambiguous and new facts are found during implementation. The biggest problem I've had working with software developers has always been the lag between seeing the results and steering towards the right direction, not the skills of the developer (with many exceptions of course).
Regardless, working on the wrong things is time wasted. And again, I'm procrastinating here while waiting for Fable to run a benchmark on a few solutions to a problem I have. We can guess what would work, but we only know after the benchmark. A faster model, with fewer capabilities, would've been a much better choice this time... well, "git gud" they said... and live and learn! Faster model = less time for procrastination.
PS. AI models don't live and learn; the discussion about AGI is pretty pointless imo. It's a tool. Does it matter if it is AGI or not if it does what you want it to do? Does the IQ of your colleague matter if he's good at what he's supposed to do? Or bad? Well... I guess it does matter, as many people are up in arms about whether Astro is AGI or not. Personally, I think we're past the point for that debate. These are amazing tools.
by tappio - Finally, OpenAI has a Fable/Mythos class model. 5.6 Sol felt like 5.5 on steroids, probably just a different checkpoint with a lot more RL post training.
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.
by HAL3000 - The ARC-AGI-3 scorecard is extremely misleading given that it clearly states itself that "with [the responses API] harness, we estimate Sol would score in the ballpark of ~30%." but it shows a score of 7.8% for GPT-5.6 Sol presumably since if they updated the percentage for GPT-5.6 Sol to the score it would receive with the responses API harness they used for GPT-6 Astra they'd have to do the same for the percentage they show for Opus 5 which would similarly be much higher.
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.
by intenex - GPT 6 Astra benchmarks https://cdn.thenewstack.io/media/2026/09/358eb84a-screenshot...
Performance is significantly higher than Fable 5.1
Source: https://thenewstack.io/openai-gpt6-astra-benchmarks/
by tristanj - I finally got access. Here are the pelicans! https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
The "max" one at the bottom took 4 minutes 2 seconds and cost 63.206 cents.
For comparison, here those new Astra pelicans are in a grid with the GPT-5.6 pelicans: https://static.simonwillison.net/static/2026/gpt-6-and-5.6-p...
by simonw - I think the thing I'm most excited about is the increase in _user prompting_.
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?
by jumploops - I can’t help but notice how much this echoes Francois Chollet’s On the Measure of Intelligence: https://arxiv.org/abs/1911.01547
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.
by astrobiased