I'd like to mention the Little Dorrit Benchmark [1] which I have been running for a couple of years now. It has a few nice features:
1. It tests visual reasoning and structured output in a single task.
2. It seems to sort correctly on advancing general intelligence. As a counterexample, if I'm not misremembering, artificialanalysis.ai made some changes to their benchmark recently after Astra ranked below several older models.
3. While models have gotten significantly better in the past 2 years, the top model is still at 0.78 F1, so the test is not yet saturated. As a reference point, when I started, the top models were in the [0.1, 0.2] range.
Website looks very cool, Fable's octopus-organist looks very cute, but I feel like this benchmark (generate an SVG by a short and slightly ridiculous description) in general has been completely Goodharted [0].
I think they all just added a bunch of similar tasks to their training sets, so we cannot judge true emergent capabilities of the models anymore.
I don't think a bunch of similar tasks can really saturate the "create a SVG of X", because the model should have a quite good spatial understanding of the world and how everything interacts.
For example Gemini 3.8 Flash seems very impressive at first glance but the results are not actually very coherent, this shows that its "world model" is not particularly great (compare to SOTA models).
Anyone else surprised the generations look so remarkably similar? All of these models have “independently” generalized that the moose should roughly be standing at the same position (left) or that the giraffe should have a certain color palette.
With respect to bikes (with or without pelicans) there's a strong natural bias because people displaying bikes tend to want to show off the side with the gears.
More-generally, I suspect an influence from how left-to-right languages (i.e. English) affect comic layouts. Overcoming that bias often means using vertical space to exploit the top-to-bottom habit instead. (Consider the rarity of an English-language comic panel where action is from bottom-right to top-left.)
All models are pretty good now at generating these images. Back in the day, I remember experimenting with the pelican images and most of the models couldn't align the legs with the wheels. Right now as well, GPT messed up an octopus leg by originating it through the instrument rather than the octopus itself.
I think that intertwining two entities (living/non-living) is still challenging but overall they're pretty sound.
Its giraffe / grandfather clock one is pretty bad... (two necks? wearing a suit?)
Weird as well, it's clearly pulled out some 1884 patent on clock designs, and a quick ddg/google doesn't show it as anything to do with grandfather clocks.
Better than Astra and Fable? It looks quite pretty and even impressive at times if you squint, but look closer and it falls apart in terms of coherency. And I say that as somebody who mains Gemini 3.8.
I'd like to mention the Little Dorrit Benchmark [1] which I have been running for a couple of years now. It has a few nice features:
1. It tests visual reasoning and structured output in a single task.
2. It seems to sort correctly on advancing general intelligence. As a counterexample, if I'm not misremembering, artificialanalysis.ai made some changes to their benchmark recently after Astra ranked below several older models.
3. While models have gotten significantly better in the past 2 years, the top model is still at 0.78 F1, so the test is not yet saturated. As a reference point, when I started, the top models were in the [0.1, 0.2] range.
[1] https://dorrit.pairsys.ai/
Website looks very cool, Fable's octopus-organist looks very cute, but I feel like this benchmark (generate an SVG by a short and slightly ridiculous description) in general has been completely Goodharted [0].
I think they all just added a bunch of similar tasks to their training sets, so we cannot judge true emergent capabilities of the models anymore.
[0] https://en.wikipedia.org/wiki/Goodhart%27s_law
I don't think a bunch of similar tasks can really saturate the "create a SVG of X", because the model should have a quite good spatial understanding of the world and how everything interacts.
For example Gemini 3.8 Flash seems very impressive at first glance but the results are not actually very coherent, this shows that its "world model" is not particularly great (compare to SOTA models).
Anyone else surprised the generations look so remarkably similar? All of these models have “independently” generalized that the moose should roughly be standing at the same position (left) or that the giraffe should have a certain color palette.
With respect to bikes (with or without pelicans) there's a strong natural bias because people displaying bikes tend to want to show off the side with the gears.
More-generally, I suspect an influence from how left-to-right languages (i.e. English) affect comic layouts. Overcoming that bias often means using vertical space to exploit the top-to-bottom habit instead. (Consider the rarity of an English-language comic panel where action is from bottom-right to top-left.)
All models are pretty good now at generating these images. Back in the day, I remember experimenting with the pelican images and most of the models couldn't align the legs with the wheels. Right now as well, GPT messed up an octopus leg by originating it through the instrument rather than the octopus itself.
I think that intertwining two entities (living/non-living) is still challenging but overall they're pretty sound.
> All models are pretty good now at generating these images.
That's pretty generous.
All models are pretty good now at generating these images.
Not zebras. If you want to see how bad SVG output still is, ask for a zebra riding a scooter.
Would love to see Qwen3.8-27b here, since that is the model most people are running locally.
Feels like google has a different training set than the others?
I notice none of the octopi seem to be actually facing the organ.
It is interesting how similar the designs are across the models.
Gemini 3.8 flash seems to (subjectively) be the outlier in terms of performance to cost ratio?
Its giraffe / grandfather clock one is pretty bad... (two necks? wearing a suit?)
Weird as well, it's clearly pulled out some 1884 patent on clock designs, and a quick ddg/google doesn't show it as anything to do with grandfather clocks.
Gemini 3.8 Flash results are often not very coherent but it does put a lot of shading and details to hide the fact.
Does a test of instructions how to fold origami figures in a SVG/jpeg exist? Or could be useful?
Interesting, out of all examples Gemini 3.8 is the best for me. Also the image style is different and more vibrant than others
Better than Astra and Fable? It looks quite pretty and even impressive at times if you squint, but look closer and it falls apart in terms of coherency. And I say that as somebody who mains Gemini 3.8.
Well that benchmark is now saturated, what next. How fast you can hack the pentagon?
> An elephant typing on a typewriter
A monkey, surely?
Asking to animate it add an interesting layer of difficulty
I noticed in the "A penguin juggling chainsaws" prompt that Qwen created an animated svg
I'd say at least half of Qwen's 2026 runs are animated.
The only other one I've spotted is animated is Gemini 3.0's 2025 run of an elephant.
Why is this a good test?
The 3 US models have their own style.
Qwen3.8 is very clearly distilled from Claude models.
Try asking an LLM to draw you the cool S.