Maybe it gets posted every time because besides a personal believe by the person popularising this "benchmark", there is no reason to assume that certain labs aren't intentionally training to game this and every other lab at least unintentionally gets improvements for this specific combination of animal and action because the internet is full of both good and bad examples, often ranked, which does inevitably become training data.
I have shared examples of certain models by certain labs doing far better on the pelican cycling vs other, similar prompts. Just operating on a feeling that labs don't optimise for this (as mentioned, even if they don't training data is filled with these) is not solid enough that criticism shouldn't be leveraged when it comes up.
> I have shared examples of certain models by certain labs doing far better on the pelican cycling vs other, similar prompts
Please share those again!
One of the things I'm most looking forward to is a lab producing a model that creates a really great pelican riding a bicycle and then a terrible sloth riding a skateboard (or whatever).
Evidence in the other direction (that they're able to generalize) is that I can't think of any LLM currently that can't create usable (placeholder) SVG icons, I tried a bit before the pelican became popular and it was abysmal.
Happy to, here one example where Grok 4 Fast, despite producing a fairly consistent pelican [0], did severely worse in a similarly outlandish scenario along with Haiku 4.5 and GPT-5 for context: https://news.ycombinator.com/item?id=45599403
> [...] a really great pelican riding a bicycle and then a terrible sloth riding a skateboard [...]
Happy to play ball. You made a blog post a few weeks back on one of the Qwen models with the eye-catching title "Qwen3.6-35B-A3B on my laptop drew me a better pelican than Claude Opus 4.7" [1].
Here is what Qwen3.6-35B-A3B via Openrouter provided for a sloth riding a skateboard: https://imgur.com/a/Dy8fvR5
Like Grok 4 Fasts attempt at a mushroom in a rowboat, it is barely recognisable as anything despite both Qwen3.6-35B-A3B and Grok 4 Fast having no issue with more popular (i.e. benchmarked) examples. Whether this is a case of training data being unsanitized or intentional benchmark targeted training, I cannot say, but it is the case.
A massive delta in favour of Opus 4.7, despite the pelican Qwen3.6-35B-A3B produced being noticeably better as you rightly pointed out. What does that tell us? Whether intentional or not (with such deltas, I do have my suspicions), any eval with such a delta is clearly polluted and can not be a source of information, especially as its continued existence does hinge on you testing similar prompts in private as a sanity check, yet by your own admission never noticing the plainly apparent delta in quality. I specifically stuck with the skateboarding sloth too, to keep it as fair as possible and found this in less than 5 minutes...
I would not critique your use of this fun benchmark the way I tend to if I did not have evidence to back up my position, including private evals beyond SVGs that I can reliably use to point out major deviations between what a models claimed performance is according to major benchmarks vs the actual performance outside these known test cases.
I will also say that while I have a lot to be critical of regarding Anthropics modus operandi, especially how they present interesting findings like their j-space work, which I found was irresponsibly anthropomorphic in their reporting, especially as this wasn't a first in model interpretability, but mainly a leap due to being applied to a larger model, but of all the labs, they are the ones that never underperform my evals vs public ones and they appear to strictly keep their training data sanitised.
Happy to discuss public vs private evals and the merit of each if you'd like, I do appreciate your reporting in general but just think the SVG benches have become evidently polluted, which is also why even simple queries in my benchmarks are private. Just saw Thinking Machines Inkling model succeed in certain queries that neither Fable 5, nor GPT-5.6 Sol on any reasoning level managed, which I feel is valuable to truly gauge where we are at. Informs my work with models, my views of the industry and my assessment of the future these tools have, along with how to best implement them to enable better UX.
Respectfully, did you? The comment was specific to doubting the believe simonw has that labs are not training [0] specifically for this task, which is exactly what simonw wrote in the post [1], that it is a believe of his that they don't. He did not mention any kind of evidence or any piece of information that would indicate that the commenter didn't read the blog post.
Did you read either the post or the comment it was referencing?
On the note of training on SVGs, I have seen some labs models outperform when prompted for SVGs of certain animal and action combinations (pelican on bike, panda eating burger, etc.) compared to other similarly outlandish prompts for SVG output that are not part of widely reported benchmarks, even shared evidence one of the last times this came up on here.
I'll note there's a difference between "pelicans on bikes aren't part of the training set" and "I’m still not convinced that labs are training for the benchmark".
I'm sure all sorts of crap pelican riding bicycle SVGs have ended up in the huge crawls of data that the labs feed into their pre-training steps.
What I'm questioning here is that there are labs who have sat down and deliberately tested and tweaked the performance for this particular task, independent of general model improvements.
The one exception here is Gemini, who have clearly invested a lot of effort in SVG tasks. I have no idea if my stupid benchmark influenced that decision!
Gemini have boasted about how good they are at pelicans riding bicycles, frogs on penny-farthings, giraffes driving a tiny car, ostriches on roller skates, turtles kickflipping skateboards, and dachshunds driving a stretch limousine. So if they trained for the test they did at least expand it a whole bunch! https://twitter.com/JeffDean/status/2024525132266688757
> What I'm questioning here is that there are labs who have sat down and deliberately tested and tweaked the performance for this particular task, independent of general model improvements.
We are going from pretty good pelican to jumbled mess with a similarly silly, but different prompt across multiple models from multiple labs, both Western and Eastern, both Open Weight and Closed.
Are they responding to: “I’m still not convinced that labs are training for the benchmark—if they were, I’d expect much better results.”
Clearly not. There's a subset of HN users who rush to post this same thing every single time.
Maybe it gets posted every time because besides a personal believe by the person popularising this "benchmark", there is no reason to assume that certain labs aren't intentionally training to game this and every other lab at least unintentionally gets improvements for this specific combination of animal and action because the internet is full of both good and bad examples, often ranked, which does inevitably become training data.
I have shared examples of certain models by certain labs doing far better on the pelican cycling vs other, similar prompts. Just operating on a feeling that labs don't optimise for this (as mentioned, even if they don't training data is filled with these) is not solid enough that criticism shouldn't be leveraged when it comes up.
> I have shared examples of certain models by certain labs doing far better on the pelican cycling vs other, similar prompts
Please share those again!
One of the things I'm most looking forward to is a lab producing a model that creates a really great pelican riding a bicycle and then a terrible sloth riding a skateboard (or whatever).
I've not seen that myself yet.
Evidence in the other direction (that they're able to generalize) is that I can't think of any LLM currently that can't create usable (placeholder) SVG icons, I tried a bit before the pelican became popular and it was abysmal.
Happy to, here one example where Grok 4 Fast, despite producing a fairly consistent pelican [0], did severely worse in a similarly outlandish scenario along with Haiku 4.5 and GPT-5 for context: https://news.ycombinator.com/item?id=45599403
> [...] a really great pelican riding a bicycle and then a terrible sloth riding a skateboard [...]
Happy to play ball. You made a blog post a few weeks back on one of the Qwen models with the eye-catching title "Qwen3.6-35B-A3B on my laptop drew me a better pelican than Claude Opus 4.7" [1].
Here is what Qwen3.6-35B-A3B via Openrouter provided for a sloth riding a skateboard: https://imgur.com/a/Dy8fvR5
Like Grok 4 Fasts attempt at a mushroom in a rowboat, it is barely recognisable as anything despite both Qwen3.6-35B-A3B and Grok 4 Fast having no issue with more popular (i.e. benchmarked) examples. Whether this is a case of training data being unsanitized or intentional benchmark targeted training, I cannot say, but it is the case.
And here is Opus 4.7, again via Openrouter: https://imgur.com/a/Qus1Enf
A massive delta in favour of Opus 4.7, despite the pelican Qwen3.6-35B-A3B produced being noticeably better as you rightly pointed out. What does that tell us? Whether intentional or not (with such deltas, I do have my suspicions), any eval with such a delta is clearly polluted and can not be a source of information, especially as its continued existence does hinge on you testing similar prompts in private as a sanity check, yet by your own admission never noticing the plainly apparent delta in quality. I specifically stuck with the skateboarding sloth too, to keep it as fair as possible and found this in less than 5 minutes...
I would not critique your use of this fun benchmark the way I tend to if I did not have evidence to back up my position, including private evals beyond SVGs that I can reliably use to point out major deviations between what a models claimed performance is according to major benchmarks vs the actual performance outside these known test cases.
I will also say that while I have a lot to be critical of regarding Anthropics modus operandi, especially how they present interesting findings like their j-space work, which I found was irresponsibly anthropomorphic in their reporting, especially as this wasn't a first in model interpretability, but mainly a leap due to being applied to a larger model, but of all the labs, they are the ones that never underperform my evals vs public ones and they appear to strictly keep their training data sanitised.
Happy to discuss public vs private evals and the merit of each if you'd like, I do appreciate your reporting in general but just think the SVG benches have become evidently polluted, which is also why even simple queries in my benchmarks are private. Just saw Thinking Machines Inkling model succeed in certain queries that neither Fable 5, nor GPT-5.6 Sol on any reasoning level managed, which I feel is valuable to truly gauge where we are at. Informs my work with models, my views of the industry and my assessment of the future these tools have, along with how to best implement them to enable better UX.
[0] https://simonwillison.net/2025/Sep/20/grok-4-fast/
[1] https://simonwillison.net/2026/Apr/16/qwen-beats-opus/
Respectfully, did you? The comment was specific to doubting the believe simonw has that labs are not training [0] specifically for this task, which is exactly what simonw wrote in the post [1], that it is a believe of his that they don't. He did not mention any kind of evidence or any piece of information that would indicate that the commenter didn't read the blog post.
Did you read either the post or the comment it was referencing?
On the note of training on SVGs, I have seen some labs models outperform when prompted for SVGs of certain animal and action combinations (pelican on bike, panda eating burger, etc.) compared to other similarly outlandish prompts for SVG output that are not part of widely reported benchmarks, even shared evidence one of the last times this came up on here.
[0] ... incredible Simon still believes ...
[1] I’m still not convinced that labs ....
I'll note there's a difference between "pelicans on bikes aren't part of the training set" and "I’m still not convinced that labs are training for the benchmark".
I'm sure all sorts of crap pelican riding bicycle SVGs have ended up in the huge crawls of data that the labs feed into their pre-training steps.
What I'm questioning here is that there are labs who have sat down and deliberately tested and tweaked the performance for this particular task, independent of general model improvements.
The one exception here is Gemini, who have clearly invested a lot of effort in SVG tasks. I have no idea if my stupid benchmark influenced that decision!
Gemini have boasted about how good they are at pelicans riding bicycles, frogs on penny-farthings, giraffes driving a tiny car, ostriches on roller skates, turtles kickflipping skateboards, and dachshunds driving a stretch limousine. So if they trained for the test they did at least expand it a whole bunch! https://twitter.com/JeffDean/status/2024525132266688757
> What I'm questioning here is that there are labs who have sat down and deliberately tested and tweaked the performance for this particular task, independent of general model improvements.
Given the massive delta easily reproducible with some models, is it really doubtful that certain labs have not: https://news.ycombinator.com/item?id=48951229
We are going from pretty good pelican to jumbled mess with a similarly silly, but different prompt across multiple models from multiple labs, both Western and Eastern, both Open Weight and Closed.