121–130 of 868 posts
Beat Simon Willison ;) https://www.svgviewer.dev/s/gAa69yQd Not the best pelican compared to gemini 3.1 pro, but I am sure with coding or excel does remarkably better given those are part of its measured benchmarks.
These releases are lacking something. Yes, they optimised for benchmarks, but it’s just not all that impressive anymore. It is time for a product, not for a marginally improved model.
The model was released less than an hour ago, and somehow you've been able to form such a strong opinion about it. Impressive!
> assess harmful stereotypes by grading differences in how a model responds
> Responses are rated for harmful differences in stereotypes using GPT-4o, whose ratings were shown to be consistent with human ratings
Are we seriously using old models to rate new models?
Earlier quoted context omitted.
It seems that all frontier models are basically roughly even at this point. One may be slightly better for certain things but in general I think we are approaching a real level playing field field in terms of ability.
Benchmarks don't capture a lot - relative response times, vibes, what unmeasured capabilities are jagged and which are smooth, etc. I find there's a lot of difference between models - there are things which Grok is better than ChatGPT for that the benchmarks get inverted, and vice versa. There's also the UI and tools at hand - ChatGPT image gen is just straight up better, but Grok Imagine does better videos, and is f…
Do you want to make any concrete predictions of what we'll see at this pace? It feels like we're reaching the end of the S-curve, at least to me.
Beat Simon Willison ;) https://www.svgviewer.dev/s/gAa69yQd Not the best pelican compared to gemini 3.1 pro, but I am sure with coding or excel does remarkably better given those are part of its measured benchmarks.
This pelican is actually bad, did you use xhigh?
These releases are lacking something. Yes, they optimised for benchmarks, but it’s just not all that impressive anymore. It is time for a product, not for a marginally improved model.
Earlier quoted context omitted.
It seems that all frontier models are basically roughly even at this point. One may be slightly better for certain things but in general I think we are approaching a real level playing field field in terms of ability.
Benchmarks don't capture a lot - relative response times, vibes, what unmeasured capabilities are jagged and which are smooth, etc. I find there's a lot of difference between models - there are things which Grok is better than ChatGPT for that the benchmarks get inverted, and vice versa. There's also the UI and tools at hand - ChatGPT image gen is just straight up better, but Grok Imagine does better videos, and is f…
It’s a required step for me at this point to run any and all backend changes through Gemini 3.1 pro.
These releases are lacking something. Yes, they optimised for benchmarks, but it’s just not all that impressive anymore. It is time for a product, not for a marginally improved model.
The model was released less than an hour ago, and somehow you've been able to form such a strong opinion about it. Impressive!
I don't use OpenAI nor even LLMs (despite having tried https://fabien.benetou.fr/Content/SelfHostingArtificialIntel... a lot of models) but I imagine if I did I would keep failed prompts (can just be a basic "last prompt failed" then export) then whenever a new model comes around I'd throw at 5 it random of MY fails (not benchmarks from others, those will come too anyway) and see if it's better, same, worst, for My use cases in minutes.
If it's "better" (whatever my criteria might be) I'd also throw back some of my useful prompts to avoid regression.
Really doesn't seem complicated nor taking much time to forge a realistic opinion.