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GPT-6 Astra

openai.com

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Re: GPT-6 Astra

#771

Post by huijzer »

intenex wrote: post #272

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…

> I am reasonably confident that there's essentially nothing that I am better than Fable at

Most things in the real world probably. I’m not saying AI can’t do it, but currently it’s bad. Try send an image of the inside of a broken toaster and how to fix. It’s laughable. Again, not saying AI will never do it, but am saying there are definitely large holes in knowledge.

Re: GPT-6 Astra

#772

Post by xixixao »

intenex wrote: post #272

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…

Dumb single sample example: I asked Fable 5.1 to change from hard to soft deletion in an office map backend, and it used soft deletion for data which is synced from another system, but left hard deletion on for the mapping data itself (who sits where). For me it’s a pretty severe lack of judgement (like a red flag if I asked this in an interview).

Re: GPT-6 Astra

#773

Post by znnajdla »

> With Sites (opens in a new window) in ChatGPT, Astra can create, host, and share websites, web apps, and games directly from a prompt.

Oops, shots fired. A direct attack on the vibe coded app market. Replit, Lovable, etc.

Re: GPT-6 Astra

#774

Post by dudeinhawaii »

Bjorkbat wrote: post #661
rjtc wrote: post #563

I am really confused on how it can saturate ARC-AGI but still perform poorly on aggregated benchmarks: https://artificialanalysis.ai/models Perhaps if it was allowed this custom harness for all benchmarks it would similarily saturate?

The most straightforward answer is that despite efforts to design a benchmark that, in theory, is supposed to measure generalizable intelligence, performance on ARC-AGI-3 can't be reliably correlated to performance anywhere else. I kind of lost faith in it after o1 or o3, I can't remember which, absolutely crushed ARC-AGI-1. And, you know, maybe also some funny business. I think it's good to be a little suspicious of…

I don't think this is quite true. We have other examples.

Fable is without question the larger and more thoughtful/intelligent model. It also gets out performed by Opus on many/most benchmarks. So we can say that while Fable is more intelligent, Opus is more capable. I'd still opt for Fable in nearly every case if tokens were free.

So it can be true that the "smarter" model is perhaps not the smartest in every single niche dimension that its cousins have been fine-tuned for (yet!).

Re: GPT-6 Astra

#775

Post by jrflo »

rjtc wrote: post #563

I am really confused on how it can saturate ARC-AGI but still perform poorly on aggregated benchmarks: https://artificialanalysis.ai/models Perhaps if it was allowed this custom harness for all benchmarks it would similarily saturate?

Idk, they’re trying to sell a $500/mo/seat service to tell you what model is best. I think it’s in their interest to keep it confusing and opaque. Not exactly independent.

Re: GPT-6 Astra

#776

Post by edg5000 »

Sol has been very effective at schematic design (using Skidl) and at reviewing PCB layouts. But layout was still done manually by me. I'm very impressed and surprised to see they exactly a demo of Astra doing PCB layout. This is could be a game changer for electrial engineering! It already is since the schematic (and library management) is where a lot of the design work goes.

Re: GPT-6 Astra

#777

Post by kulkarniamey »

The model is probably excellent. The problem here is AGI having various definitions and many of them getting narrowed down to whatever makes benchmark numbers look good.

Re: GPT-6 Astra

#778

Post by bdelmas »

abixb wrote: post #247

I want to take a step back: So, this is GPT-6 -- the natural number version release comparable to GPT-4 and GPT-5 from the past few years. The ARC-AGI-3 score is obviously impressive at 99.9% (we'll need to wait for more details on how they used the response API harness on GPT-6 Astra, wrt reasoning retention and compaction), but every other benchmarks seems to be a relatively modest improvement, comparable with any…

People really believe in this AGI marketing?
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