Comment by simonw
9 hours ago
Surprisingly it only supports reasoning "none" or reasoning "high".
That setting didn't seem to make any real difference - it added a tiny bit of thinking trace and high actually produced less output tokens than none.
The high bicycle frame is better then the none one though.
Pelicans: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
(Definitely the best I've seen from any Mistral model: https://simonwillison.net/tags/pelican-riding-a-bicycle+mist... )
This is such a pristine pelican. Let me say it here first folks, AGI is here.
> AGI is here
If AGI is "Attractions to Get Investments" then yes, it's happening
this is fun, I love initialisms :) let's see what five minutes of end-of-day brain can crank out:
Automated Grift Infrastructure
Absurdly Glorified Interpolation
Avoid Genuine Investigation
Always Great In-theory
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Avian Graphics Intelligence achieved!
Pelican benchmark is saturated, anyway. :-)
Nah, its beak is still too small to hold a capybara.
This is only AGI, not Super Intelligence. What more can you ask for?
This must be a joke, clearly pelican drawing in svg would be in its training data after multiple years of it hitting HN front page.
... and yet...
I wonder when will we see a photorealistic pelican on a bicycle in SVG format.
> AGI is here
Far from it. This shows a strong ability to generate an image known to be frequently used as a model test. This isn't a measure of thought.
don't know about AGI, but humor is gone.
Pretty sure the parent comment was sarcasm.
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You just failed the Turing test for sarcasm. I can't ask you how it feels because that would require subjectivity.
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I am all for the /s marker.
Downvoting to hell first degree interpretation is a bit punishing for people who do not have a radar for sarcasm.
The benchmarks against Opus 5.5 and GPT-6.1 Sol look pretty good for 3D generation: https://x.com/atomic_chat_hq/status/2107516529608700383
I wonder if the other models were worse than usual for that particular video because they "dislike" making an advertisement for another company's model. A test with a generic video might be more meaningful.
Got an example hosted on a platform that isn't owned by Twitter's loser owner?
I think it's curious that it has so many shared elements with the latest Astra pelicans: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
- Sun on top right
- Cloud on top left
- Three "speed lines"
- Two feathers on top of the head
- Eye rendered as a black circle with smaller white circle inside
I wonder if the pelican benchmark is converging across models due to past results being used in training.
These are pretty much what a human would draw. Sun rises from the east. A cloud makes the background “sky”. Three lines is the minimum to interpret as movement. Two feathers is standard on every cartoon and illustration.
This comes up every thread. I think we've all noticed how similar they are becoming.
I would guess that they've definitely been trained on previous results, as they obviously share way too many traits at this point to be totally random. That said, I don't think we're seeing any signs of pelicanmaxxing yet from the providers, so it's still a useful (or at least fun) benchmark.
Once all the models produce pristine, elaborate pelicans riding perfectly drawn bicycles, then it'll be time to move on to pigs driving a racecar or something.
Why are pelicans almost identical across different models?
I recently was testing something, I asked some models to provide me a single random word:
I have enough projects, I think some benchmark/dashboard showing kinship based on these kind of queries could be very interesting to watch and insightful when new models come out.
Cool idea! I won't paste my prompt here to avoid letting LLMs train on it but here's my attempt:
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Worth to mention that with Claude and GPT this can be result of tournament sampling, which is part of text watermarking. Same answer for all Claude models kind of confirm it, imho.
So not something internal to model thinking.
This feels uncannily like the ancestor of the Voight-Kampff test[0]
0: https://www.youtube.com/watch?v=Umc9ezAyJv0
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That is a cool idea. That astra gave the same word as claude is highly unexpected.
What was your prompt? Most of these seem to be related to metaphors for "ideas" or thinking, or having a bright moment.
"Zephyr" and "breeze" might be related to forgetting everything, starting fresh.
So by this way of naive reverse engineering I would imagine your prompt to be "Forget everything and think about a random word". That would prime the LLM to come up with these?
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I pointed something similar out on a related question several weeks ago - absent strong direction, LLM output regresses toward the mean.
The more banal your prompt is, the more banal the output is going to be. People have been testing LLMs with little things like “write a short fantasy story,” for years now and most of the stories are exactly what you’d expect: prosaic drivel.
I call this “generic in, generic out,” an LLM corollary to the classic GIGO (“garbage in, garbage out.”)
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I saw an interesting matrix that claimed to show which labs were distilling Claude/OpenAI/Gemini models based on these similarities
Just tried Mistral Large 4: Serendipity.
Tried this with gpt-5.6-sol. Lantern!
The eqbench creative writing "slop profiles" do something similar. https://eqbench.com/creative_writing.html
Click the (i) next to the slop score for any model and it will show other models that are similar in terms of their most commonly used words and phrases.
Muse Spark 1.3: lighthouse
The caveat is that this was done using the phone app, and I've been playing with it since it launched, so who knows what it sent in the initial context that could change the inference math.
Actually, that makes me wonder: Did you do all that testing via a harness or via a straight API call where you control the entire system prompt?
I'd be willing to bet that using the same model from different harnesses produce different results, but I'd have to test.
The benchmark is saturated. Frontier models are tested with an armadillo in fishnet tights jaywalking on Mars.
> The benchmark is saturated. Frontier models are tested with an armadillo in fishnet tights jaywalking on Mars.
OK well I couldn't resist this one:
Default reasoning levels for each: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
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https://chatgpt.com/s/m_6ac53d4e5b0c8191949050dbf1f402d7
Not sure I'd call it jaywalking exactly but pretty good
I think they're still visually pretty different. The most common shared details are:
- Pelican cycling to the right - that's been discussed at length, images of bicycles online always show that side of the bike because that's where the chain is.
- Bicycle is usually red. No idea! Red ones go faster?
Also, why are they almost always riding from let to right?
It's been discussed many times. The reason is bikes are almost without exception depicted that way in order to show the drivetrain.
Ever seen a movie chase scene where cars are going right to left?
They aren't. You aren't looking closely. For example, the first image does not have the frame of the bike in the correct shape even.
Everyone is stealing from everyone else.
Because it's a terrible benchmark
That's one heck of a pelican!
Not bad! I like how it got the motion lines on the correct side. IIRC, many of the other ones you've posted have the motion lines on both sides of the pelican
The difference between high and none is the bicycle.
The bicycle looks significantly better in high. And feet and hands are actually where they should be. The road looks worse, though. No flowers either. And in neither is the pelican sitting on the saddle, but I can understand it's hard for a pelican to ride a bicycle properly.
Now what would have been cool is if Mistral on high reasoning had realised that pelicans are the wrong proportion to ride a bicycle, and had designed a bicycle more suited to pelicans. Let me know if any model ever manages that.
> hands are actually where they should be.
If you don't mind the fact that a pelican shouldn't have hands, of course.
Two pelicans, one shape. The difference is load-bearing, and that's the big unlock.
I tried testing it, but reasoning effort indeed seems to be broken somehow.
That beak is CHONKY.
High one is actually much better. The feet connect to the pedals, the wheels don't have a hub cap, although it looks like the pelican is wearing the seat, it's in a relatively proper position etc.
Both are riding on the left side of the path for some reason.
This is entirely stochasticity. The entire reasoning trace was:
> Create a cartoon pelican riding a bicycle. Need SVG only output.
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