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Pretty great pelican: https://simonwillison.net/2026/Feb/19/gemini-31-pro/ - took over 5 minutes though, but I think that's because they're having performance teething problems on launch day.
In their blog post[1], first use case they mention is svg generation. Thus, it might not be any indicator at all anymore.
[1] https://blog.google/innovation-and-ai/models-and-research/ge...
Pretty great pelican: https://simonwillison.net/2026/Feb/19/gemini-31-pro/ - took over 5 minutes though, but I think that's because they're having performance teething problems on launch day.
Earlier quoted context omitted.
Simons been doing this exact test for nearly 18 months now, if vendors want to benchmaxx it then they've had more than enough time to do so already.
Exactly. As far as I'm concerned, the benchmark is useless. It's way too easy and rewarding to train on it.
Implementation and Sustainability Hardware: Gemini 3 Pro was trained using Google’s Tensor Processing Units (TPUs). TPUs are specically designed to handle the massive computations involved in training LLMs and can speed up training considerably compared to CPUs. TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training, which can lead to bet…
It's such an uninformative piece of marketing crap
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Ugh, the gears and chain don't mesh and there's no sprocket on the rear hub But seriously, I can't believe LLMs are able to one-shot a pelican on a bicycle this well. I wouldn't have guessed this was going to emerge as a capability from LLMs 6 years ago. I see why it does now, but... It still amazes me that they're so good at some things.
next time you host a party, have people try to draw a bicycle on your whiteboard (you have a whiteboard in your house right? you should, anyway...) human adults are generally quite bad at drawing them, unless they spend a lot of time actually thinking about bicycles as objects
Pretty great pelican: https://simonwillison.net/2026/Feb/19/gemini-31-pro/ - took over 5 minutes though, but I think that's because they're having performance teething problems on launch day.
Price is unchanged from Gemini 3 Pro: $2/M input, $12/M output. https://ai.google.dev/gemini-api/docs/pricing Knowledge cutoff is unchanged at Jan 2025. Gemini 3.1 Pro supports "medium" thinking where Gemini 3 did not: https://ai.google.dev/gemini-api/docs/gemini-3 Compare to Opus 4.6's $5/M input, $25/M output. If Gemini 3.1 Pro does indeed have similar performance, the price difference is notable.
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Less pretty and more practical, it's really good at outputting circuit designs as SVG schematics. https://www.svgviewer.dev/s/dEdbH8Sw
I don't know what of this is the prompt and what was the output, but that's a pretty bad schematic (for both aesthetic and circuit-design reasons).
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Jeff Dean just posted an animated version: https://x.com/JeffDean/status/2024525132266688757
One underrated thing about the recent frontier models, IMO, is that they are obviating the need for image gen as a standalone thing. Opus 4.6 (and apparently 3.1 Pro as well) doesn't have the ability to generate images but it is so good at making SVG that it basically doesn't matter at this point. And the benefit of SVG is that it can be animated and interactive. I find this fascinating because it literally just happ…