I think this one has advantages over the “pelican riding a bicycle” one because it hinges on an anatomical feature that many models associate with royalty, “habsburg” being a lineage and “habsburg jaw” being an anatomical feature.
Seven of fourteen models silently imported royalty into a prompt that named only an anatomical feature. Two of them knew they were extrapolating ("because Habsburg") and did it anyway.
Mistral returned byte-identical output across separate calls.
Gemini narrates its work in 65 comments; Llama says nothing.
If you're deciding which model to trust with instructions, "how much does it embellish beyond what I asked" and "does it behave deterministically" are directly practical questions.
> Two of them knew they were extrapolating ("because Habsburg") and did it anyway.
You seem to imply that they ought not to. I disagree.
I wasn't familiar with the term before this post. Having learned it, were I given the task, I think I'd be strongly tempted to do the same extrapolation.
> If you're deciding which model to trust with instructions, "how much does it embellish beyond what I asked" and "does it behave deterministically" are directly practical questions.
Agency is agency. You still need to vet what the model's output is actually permitted to control.
For those who don’t know a Habsburg jaw also known as mandibular prognathism, it is a genetic condition characterized by a protruding lower jaw, which was notably prevalent among members of the Habsburg royal family due to their history of inbreeding. This condition often resulted in significant facial deformities and difficulties with eating and speaking.
I thought this was great, and hilarious. Kudos to Opus 5, I thought it was the only one that came close to passing. Interestingly, I thought many of the failures drew the frog face OK, and they had some type of big blob for the jaw, so they knew "Hapsburg jaw" meant a protruding jaw, but it wasn't really connected to the frog face in any way that made sense.
Small side note, the first gemini-2.5-pro one totally reminded me of some sad faced meme or Pepe the frog from somewhere. Anyone know what I'm referring to, tried to find it.
Hi all, the site is getting hugged to death, thank you, was not expecting this kind of warm response. I will be working to make this more reliable, in the meantime, sign up for my newsletter: https://www.jaymollica.com/blog/
also my favorite SVG was def the google/gemini-3.6-flash
Mine is any variations on mammoths in various situations, or anthropomorphic. Since mammoths are invariably majestically going from one place to another in any of the books, models have hard time imagining anything but that.
Also try a fantasy archer with a proper bow who is not brooding, sitting in a fantasy wood :)
How do models approach SVG generation? In one version, I imagine them actually trying to reason about them as an LLM. In another, I imagine something closer to a GAN.
Check out my MacBook SVG benchmark. From my experience, it demonstrates the Real model’s behavior. However, I notice the errors it makes, which are similar to the mistakes made by the mistake model in code.
My personal human benchmark: "Jump on one leg, while reciting the national anthem of Latvia, translated to Spanish, backwards, while drawing a frog with a brush held by toes of the other leg, on the ceiling". So far they're not doing very good but I'm sure they'll improve over time.
Here is GLM 5.2 (https://codeinput.com/s/HAO0qTxw2ia) which is still inferior to Opus. I can't find Qwen 3.8 which now is my daily driver replacing GLM. This SVG test matches my experience when working with the different models. The other models can get the details right but their output is structured in a way that makes little or no sense.
I also did a timeline from 4.7 to 5.2: https://codeinput.com/s/7oK2IIA7qRO The improvements in models looks much less impressive with this test.
The secret to great interview questions and challenge tests is keeping them secret. Posting them on HN and getting them onto the front page puts them in jeopardy.
Am I the only one who thinks it's incredible that an LLM can do this, and at the same time it's ridiculous to expect it to be capable of doing it, even thought it clearly can do it?
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[ 0.27 ms ] story [ 55.9 ms ] threadSeven of fourteen models silently imported royalty into a prompt that named only an anatomical feature. Two of them knew they were extrapolating ("because Habsburg") and did it anyway.
Mistral returned byte-identical output across separate calls.
Gemini narrates its work in 65 comments; Llama says nothing.
If you're deciding which model to trust with instructions, "how much does it embellish beyond what I asked" and "does it behave deterministically" are directly practical questions.
You seem to imply that they ought not to. I disagree.
I wasn't familiar with the term before this post. Having learned it, were I given the task, I think I'd be strongly tempted to do the same extrapolation.
> If you're deciding which model to trust with instructions, "how much does it embellish beyond what I asked" and "does it behave deterministically" are directly practical questions.
Agency is agency. You still need to vet what the model's output is actually permitted to control.
Small side note, the first gemini-2.5-pro one totally reminded me of some sad faced meme or Pepe the frog from somewhere. Anyone know what I'm referring to, tried to find it.
also my favorite SVG was def the google/gemini-3.6-flash
edit: ok better now I think
Also try a fantasy archer with a proper bow who is not brooding, sitting in a fantasy wood :)
gemini-3.6-flash runs 2 and 3 responded best to the royal portrait context.
That's a pretty good benchmark
Would've wanted to see also DS4 flash.
https://playcode.io/blog/macbook-svg-benchmark
I also did a timeline from 4.7 to 5.2: https://codeinput.com/s/7oK2IIA7qRO The improvements in models looks much less impressive with this test.