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GPT-5.5

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Re: GPT-5.5

#291

Post by CamperBob2 »

seanw444 wrote: post #272
postalcoder wrote: post #234

Earlier quoted context omitted.

I made pelicans at different thinking efforts: https://hcker.news/pelican-low.svg https://hcker.news/pelican-medium.svg https://hcker.news/pelican-high.svg https://hcker.news/pelican-xhigh.svg Someone needs to make a pelican arena, I have no idea if these are considered good or not.

Can someone explain how we arrived at the pelican test? Was there some actual theory behind why it's difficult to produce? Or did someone just think it up, discover it was consistently difficult, and now we just all know it's a good test?

It's interesting to see some semblance of spatial reasoning emerge from systems based on textual tokens. Could be seen as a potential proxy for other desirable traits.

It's meta-interesting that few if any models actually seem to be training on it. Same with other stereotypical challenges like the car-wash question, which is still sometimes failed by high-end models.

If I ran an AI lab, I'd take it as a personal affront if my model emitted a malformed pelican or advised walking to a car wash. Heads would roll.

Re: GPT-5.5

#292

Post by [deleted] »

nickvec wrote: post #228

I'm conflicted whether I should keep my Claude Max 5x subscription at this point and switch back to GPT/Codex... anyone else in a similar position? I'd rather not be paying for two AI providers and context switching between the two, though I'm having a hard time gauging if Claude Code is still the "cream of the crop" for SWE work. I haven't played around with Codex much.

[deleted]

Re: GPT-5.5

#293

Post by pants2 »

Labs still aren't publishing ARC-AGI-3 scores, even though it's been out for some time. Is it because the numbers are too embarrassing?

Re: GPT-5.5

#294

Post by Night_Thastus »

mudkipdev wrote: post #160

This is 3x the price of GPT-5.1, released just 6 months ago. Is no one else alarmed by the trend? What happens when the cheaper models are deprecated/removed over time?

This is entirely expected. The low prices of using LLMs early on was totally and completely unsustainable. The companies providing such services were (and still are) burning money by the truckload.

The hope is to get a big userbase who eventually become dependent on it for their workflow, then crank up the price until it finally becomes profitable.

The price for all models by all companies will continue to go up, and quickly.

Re: GPT-5.5

#295

Post by tedsanders »

simianwords wrote: post #237
tedsanders wrote: post #203

Earlier quoted context omitted.

Yep, it's more expensive per token. However, I do want to emphasize that this is per token, not per task. If we look at Opus 4.7, it uses smaller tokens (1-1.35x more than Opus 4.6) and it was also trained to think longer. https://www.anthropic.com/news/claude-opus-4-7 On the Artificial Analysis Intelligence Index eval for example, in order to hit a score of 57%, Opus 4.7 takes ~5x as many output tokens as GPT-5.5, w…

Maybe a good idea to be more explicit about this -- maybe a cost analysis benchmark would be a nice accompaniment. This kind of thing keeps popping up each time a new model is released and I don't think people are aware that token efficiency can change.

Agreed. Would be great if everyone starts reporting cost per task alongside eval scores, especially in a world where you can spend arbitrary test-time compute. This is one thing I like about the Artificial Analysis website - they include cost to run alongside their eval scores: https://artificialanalysis.ai/

Re: GPT-5.5

#297

Post by simonw »

deflator wrote: post #232
simonw wrote: post #195

This doesn't have API access yet, but OpenAI seem to approve of the Codex API backdoor used by OpenClaw these days... https://twitter.com/steipete/status/2046775849769148838 and https://twitter.com/romainhuet/status/2038699202834841962 And that backdoor API has GPT-5.5. So here's a pelican: https://simonwillison.net/2026/Apr/23/gpt-5-5/#and-some-peli... I used this new plugin for LLM: https://github.com/simonw/llm-op…

Hmm. Any idea why it's so much worse than the other ones you have posted lately? Even the open weight local models were much better, like the Qwen one you posted yesterday.

The xhigh one was better, but clearly OpenAI have not been focusing their training efforts on SVG illustrations of animals riding modes of transport!

Re: GPT-5.5

#298

Post by kilroy123 »

pants2 wrote: post #293

Labs still aren't publishing ARC-AGI-3 scores, even though it's been out for some time. Is it because the numbers are too embarrassing?

To be fair, there's not much to report. Isn't it pretty much at 0?

Re: GPT-5.5

#299

Post by simonw »

XCSme wrote: post #222
simonw wrote: post #195

This doesn't have API access yet, but OpenAI seem to approve of the Codex API backdoor used by OpenClaw these days... https://twitter.com/steipete/status/2046775849769148838 and https://twitter.com/romainhuet/status/2038699202834841962 And that backdoor API has GPT-5.5. So here's a pelican: https://simonwillison.net/2026/Apr/23/gpt-5-5/#and-some-peli... I used this new plugin for LLM: https://github.com/simonw/llm-op…

Is this direct API usage allowed by their terms? I remember Anthropic really not liking such usage.

Apparently it's fine: https://twitter.com/romainhuet/status/2038699202834841962

Re: GPT-5.5

#300

Post by simonw »

andriy_koval wrote: post #226
simonw wrote: post #195

This doesn't have API access yet, but OpenAI seem to approve of the Codex API backdoor used by OpenClaw these days... https://twitter.com/steipete/status/2046775849769148838 and https://twitter.com/romainhuet/status/2038699202834841962 And that backdoor API has GPT-5.5. So here's a pelican: https://simonwillison.net/2026/Apr/23/gpt-5-5/#and-some-peli... I used this new plugin for LLM: https://github.com/simonw/llm-op…

what is your setup for drawing pelican? Do you ask model to check generated image, find issues and iterate over it which would demonstrate models real abilities?

It's generally one-shot-only - whatever comes out the first time is what I go with.

I've been contemplating a more fair version where each model gets 3-5 attempts and then can select which rendered image is "best".

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