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GPT-5.2

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josalhor ◴[] No.46235005[source]
From GPT 5.1 Thinking:

ARC AGI v2: 17.6% -> 52.9%

SWE Verified: 76.3% -> 80%

That's pretty good!

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verdverm ◴[] No.46235062[source]
We're also in benchmark saturation territory. I heard it speculated that Anthropic emphasizes benchmarks less in their publications because internally they don't care about them nearly as much as making a model that works well on the day-to-day
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stego-tech ◴[] No.46235583[source]
These models still consistently fail the only benchmark that matters: if I give you a task, can you complete it successfully without making shit up?

Thus far they all fail. Code outputs don’t run, or variables aren’t captured correctly, or hallucinations are stated as factual rather than suspect or “I don’t know.”

It’s 2000’s PC gaming all over again (“gotta game the benchmark!”).

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snet0 ◴[] No.46236484{3}[source]
To say that a model won't solve a problem is unfair. Claude Code, with Opus 4.5, has solved plenty of problems for me.

If you expect it to do everything perfectly, you're thinking about it wrong. If you can't get it to do anything perfectly, you're using it wrong.

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jacquesm ◴[] No.46236543{4}[source]
That means you're probably asking it to do very simple things.
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baq ◴[] No.46236779{5}[source]
I can confidently say that anecdotally you’re completely wrong, but I’ll also allow a very different definition of ‘simple’ and/or attempting to use an unpopular environment as a valid anecdotal counterpoint.
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1. verdverm ◴[] No.46238522{6}[source]
the problem with these arguments is there are data points to support both sides because both outcomes are possible

the real thing is are you or we getting an ROI and the answer is increasingly more yeses on more problems, this trend is not looking to plateau as we step up the complexity ladder to agentic system