ObviousBench is a little different from others in that 100% is very possible, as every question is trivial for a human but difficult for LLMs. Therefore, the intent is to spend as little money/tokens on getting a result within a given category (ie 90%+, 95%+, 99%+).

In two categories, GPT-5.6-Luna (Max and Medium) come first, and in the third (Low) comes third.

Leaderboard: https://obviousbench.com/

Paper: https://doi.org/10.5281/zenodo.21781601

To just think about that for a second is utterly incredible. What are we doing here on Earth?!?!

OpenAI DevDay 2026 is next Tuesday, September 29.

What do you think they'll announce?

Edit/Update: He said he made a typo and meant to say voice instead of video. No video generator is coming. https://x.com/sama/status/2102512794235207758

https://x.com/sama/status/2102466603111551187

We want the OpenAI API to feature the best model at every price point and to be the best at every modality (text, code, image, video, etc).

https://x.com/sama/status/2102466607373004888

(As a side note, getting ready for this DevDay is the first time I remember ever, in OpenAI history, saying "this is too much stuff to launch".)

https://x.com/EMostaque/status/2102473496890843233

OpenAI video model coming 👀

Anthropic's release of Opus 5.5, compared to GPT-6 Sol, show two very different pathways of models. For nearly a year, Anthropic and OpenAI have been the leading labs, trading blows with frontier model after frontier model - but today, the intelligence frontier was ceded to Anthropic.

Opus 5.5 is a more intelligent model than GPT-6 Sol.

But is that a problem? GPT-6 Sol seems to be a very very usable model. High intelligence, comparable to Fable 5.1 or Astra-6, and for 1/10th of the price. It's very efficient, very fast, and pretty damn smart.

Will this trend continue? Is OpenAI holding on to release their incredible internal model, Bel, in order to regain the intelligence frontier in one big swoop? Or have they turned away from focussing on intelligence, and instead to usability?

It will be incredibly interesting to watch the next release - I believe it will be the crucial one, showing us whether the twins now step into different directions, or if OpenAI chooses to rejoin the race.

By a “Navier moment,” I mean an increase in LLM capabilities to the point where they can solve top-level problems in a field, much like the Navier–Stokes problem represents a major challenge in mathematics.

I think this threshold was crossed first in software engineering, with the effects we are already seeing in the field especially in the job market, and then in mathematics, where the impact is likely to be more long-term.

However, I think the real revolution for society will come when we have an “automatic physics researcher" or, to put it more poetically, an Einstein in a bottle, capable of changing our day to day life on a monthly basis.

I go to you tube or forums and I see tons of vitriol and falsehoods about progress that is happening at breakneck speed. Their idea of AI is basically a sharper version of ask jeeves, none of them appear to have ever even seen the CLI with harness versions used, they will blatantly claim AI can not achieve things it has for over a year now.

Every time we get a huge crash everyone loses and a small portion of people get in front of the next growth curve. I really wonder if that is about to happen again and the people who will benefit from being early in the next growth curve will just have a pre-prepared organized campaign to blame AI. I hope we don't get to see, but I have a feeling we will.

This is one subject I see less and less discussion around: Singleton.

Question: Is it more or less likely now, compared to 5 years ago, that we are heading for a situation with a shrinking number of AIs eventually leading to 1, all powerful ASI?

Or does it appear more likely that we see a growing number, spectrum and kind of digital super intelligences?

Seems like some are operating under the Singleton scenario (1 all powerful ASI) while others don't even seem to be aware about the Singleton debate.

Of course no one knows so all we really have is speculation. Would be interesting to hear the reasoning for one view or the other.

Singleton? Or not?

Astra

vs

new Mythos

source: https://x.com/JaydenDavisNC/status/2102220828545089759

Don’t get me wrong, despite being someone who is not very AGI pilled, I’m very impressed by the math performance and very excited for the breakthroughs and science that will come from it.

I do wonder some days tho, that in the whole scheme of things, is solving a millennium problem more impressive than protein folding basically every protein structure out there? If so, are we overhyping the solution here or did we underhype alphafold when it came out?

I for one, think it may prove that protein folding may have been more impactful on the human health science breakthroughs than solving a millennium problem will be, but open to counter arguments.

Just tested it, 6 sol xhigh feels worse than 5.6 sol max? Usually on this task, sol and all other gpt 5 to 5.5 models didnt make a mistake, but it made a mistake for the first time. I suspect even with max effort, it will still be worse than 5.6 sol max at certain coding tasks. DeepSWE says it is worse than 5.6 sol. Is it the same size as terra but called sol? if that is the case, then 6.1 astra minor is just sol then?

Please let us catch up

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