This is a useless benchmark now a days, every model provider trains their models on making good pelicans. Some have even trained every combination of animal/mode of transportation
Going to have to disagree on the backup test. Opus flamingo is actually on the pedals and seat with functional spokes and beak. In terms of adherence to physical reality Qwen is completely off. To me it's a little puzzling that someone would prefer the Qwen output.<p>I'd say the example actually does (vaguely) suggest that Qwen might be overfitting to the Pelican.
Qwen's flamingo is artistically far more interesting. It's a one-eyed flamingo with sunglasses and a bow tie who smokes pot. Meanwhile Opus just made a boring, somewhat dorky flamingo. Even the ground and sky are more interesting in Qwen's version<p>But in terms of making something physically plausible, Opus certainly got a lot closer
Qwen, at least, can draw a complete bicycle frame. The opus frame will snap in half and can’t steer.
Even the first one - Qwen added extra details in the background sure. But he Pelican itself is a stork with a bent beak and it's feet is cut off it's legs. While impressive for a local model, I don't think it's a winner.
It's a 3B model. It should not be this close. Debating their artistic qualities is missing the point.
Such a disconnect from the minutes I’ve lost and given up on Gemini trying to get it to update a diagram in a slide today. The one shot joke stuff is great but trying to say “that is close but just make this small change” seems impossible. It’s the gap between toy and tool.
I understand the 'fun factor' but at this point I really wonder what this pelican still proofs ? I mean, providers certainly could have adapted for it if they wanted, and if you want to test how well a model adapts to potential out of distribution contexts, it might be more worthwhile to mix different animals with different activity types (a whale on a skateboard) than always the same.
That's why I did the flamingo on a unicycle.<p>For a delightful moment this morning I thought I might have finally caught a model provider cheating by training for the pelican, but the flamingo convinced me that wasn't the case.
It is completely wild to me that you prefer Qwen's flamingo. I think it's really bad and Opus' is pretty good.
r/LocalLlama is now doing a horse in a racing car:<p><a href="https://redd.it/1slz38i" rel="nofollow">https://redd.it/1slz38i</a>
To me the opus flamingo is waaaay better than the qwen one. qwen has the better pelican, though.
Is a flamingo on a unicycle not merely a special case of a pelican on a bicycle?
Consider reading the article, which addresses all of the points you raise.<p>It's directly stated in the post that the entire test is meant to be humorous, not taken seriously, only that is has vaguely followed model performance to date. The author also writes that this new result shows that trend has broken..
They're certainly aware of the test, but a turtle doing a kickflip on a skateboard? I seriously doubt they train their models for that.<p><a href="https://x.com/JeffDean/status/2024525132266688757" rel="nofollow">https://x.com/JeffDean/status/2024525132266688757</a><p>If anything, the disastrous Opus4.7 pelican shows us they don't pelicanmaxx
This is a gag that's long outlived its humor, but we're in a space so driven by hype there are people who will unironically take some signal from it. They'll swear up and down they know it's for fun, but let a great pelican come out and see if they don't wave it as proof the model is great alongside their carwash test.
For coding, qwen 3.6 35b a3b solved 11/98 of the Power Ranking tasks (best-of-two), compared to 10/98 for the same size qwen 3.5. So it's at best very slightly improved and not at all in the class of qwen 3.5 27b dense (26 solved) let alone opus (95/98 solved, for 4.6).
This has similar problems to swe bench in that models are likely trained on the same open source projects that the benchmark uses.<p><a href="https://blog.brokk.ai/introducing-the-brokk-power-ranking/" rel="nofollow">https://blog.brokk.ai/introducing-the-brokk-power-ranking/</a>
If all models are trained on the benchmark data, you cannot extrapolate the benchmark scores to performance on unseen data, but the ranking of different models still tells you something. A model that solves 95/98 benchmark problems may turn out much worse than that in real life, but probably not much worse than the one that only solved 11/98 despite training on the benchmark problems.<p>This doesn't hold if some models trained on the benchmark and some didn't, but you can fix this by deliberately fine-tuning all models for the benchmark before comparing them. For more in-depth discussion of this, see <a href="https://mlbenchmarks.org/11-evaluating-language-models.html#ranking-agreement" rel="nofollow">https://mlbenchmarks.org/11-evaluating-language-models.html#...</a>
You compare tiny modal for local inference vs propertiary, expensive frontier model. It would be more fair to compare against similar priced model or tiny frontier models like haiku, flash or gpt nano.
Not when the article they're commenting on was doing literally exactly the same thing.
Eh it’s important perspective, lest someone start thinking they can drop $5k on a laptop and be free of Anthropic/OpenAI. Expensive lesson.
On thinking about the reasons this may be something at least slightly more than training on the task is the richness with which language is filled with spatial metaphors even in basic language not by laymen considered metaphor outside the field of linguistics proper, in which concepts eg Lakoff's analysis in "Metaphors we Live By and others are simply part of the field, (though unsurprisingly, among the HN crowd I've occasionally seen it brought up)<p>The amount of money you have in the bank may often "increase" or "decrease" but it also goes up and down, spatial. Concepts can be adjacent to each, orthogonal. Plenty more.<p>So, as models utilize weight more densely with more complex strategies learned during training the patterns & structure of these metaphors might also be deepened. Hmmm... another thing to add to the heap of future project-- trace down the geometry of activations in older/newer models of similar size with the same prompts containing such metaphors, or these pelican prompts, test the idea so it isn't just arm chair speculation.
I don't know what such a demo would prove in the first place. LLMs are good at things that they have been trained on, or are analogues of things they have been trained on. SVG generation isn't really an analogue to any task that we usually call on LLMs to do. Early models were bad at it because their training only had poor examples of it. At a certain point model companies decided it would be good PR to be halfway decent at generating SVGs, added a bunch of examples to the finetuning, and voila. They still aren't good enough to be useful for anything, and such improvements don't lead them to be good at anything else - likely the opposite - but it makes for cute demos.<p>I guess initially it would have been a silly way to demonstrate the effect of model size. But the size of the largest models stopped increasing a while ago, recent improvements are driven principally by optimizing for specific tasks. If you had some secret task that you knew they weren't training for then you could use that as a benchmark for how much the models are improving versus overfitting for their training set, but this is not that.
Comparing the SVGs I got for GPT-5.4, -mini and -nano at the different thinking levels was surprisingly interesting: <a href="https://simonwillison.net/2026/Mar/17/mini-and-nano/" rel="nofollow">https://simonwillison.net/2026/Mar/17/mini-and-nano/</a> (bottom of post)
That's not surprising; Opus & Sonnet have been regressing on many non-coding tasks since about the 4.1 release in our testing
Maybe the next time we suspect they're optimising for the test, switch the next test to drawing "the cure for cancer".
I'm an iguana and need to wash my bicycle in the carwash. Shall I walk or take the bus?
Between the legs and the beak I'd still rate the opus pelican higher
I'm really curious about what competes with Claude Code to drive a local LLM like Qwen 3.6?
I really wish they spent some time training for computer use. This model is incapable of finding anywhere near the correct x,y coordinate of a simple object in a picture.
I've been using Qwen3.5-35B-A3B for a bit via open code and oMLX on M5 Max with 128Gb of RAM and I have to say it's impressively good for a model of that size. I've seen a huge jump in the quality of the tool calls and how well it handles the agentic workflow.
looks like opus have been nerfed from day1
I love this benchmark!
That Qwen flamingo on the unicycle is actually quite good. A work of art.
All those models that were just at version 1.x in 2024<p>That’s so wild
I liked both of Opus' better, it was very illuminating, in both cases I didn't see the error's Simon saw and wondered why Simon skipped over the errors I saw.<p>Pelican: saturated!
I literally cannot believe that people are wasting their time doing this either as a benchmark <i>or</i> for fun. After every single language model release, no less.
It feels like the results stopped being interesting a little while ago but the practice has become part of simonw's brand, and it gives him something to post even when there is nothing interesting to say about another incremental improvement to a model, and so I don't imagine he'll stop.
I, for one, expected progress. Uneven, sometimes delayed, but ever increasing progress.<p>But that Opus pelican?
It’s not a waste of time.
As the boundaries of AI are pushed we increasingly struggle to define what intelligence actually is. It becomes more useful to test what models cannot do instead of what they can. Random tasks like the pelican test can show how general the intelligence really is, putting aside the obvious flaw that the labs can optimise for such a simple public benchmark.
Fun is so un-productive. Everyone doing things for "fun" is going to be sorry when they look back and realizes they were wasting time having a "good time" rather than optimizing their KPIs.
I can't believe you're such a party pooper. It's exciting times, the silly things do matter!
I also can't understand how this goes so viral every time on Hackernews lol
I'm currently testing Qwen3.6-35B-A3B with <a href="https://swival.dev" rel="nofollow">https://swival.dev</a> for security reviews.<p>It's pretty good at finding bugs, but not so good at writing patches to fix them.
Good reminder that these tests have always been useless, even before they started training on it.
FYI, using a 128GB M5 MacBook Pro, sourced from another article by the author.
[dead]
[flagged]
[flagged]
How about switching to MechaStalin on a tricycle? It gets kind of boring.
fuck off: <a href="https://news.ycombinator.com/item?id=47792764">https://news.ycombinator.com/item?id=47792764</a>