[yashchimata]

One thing i keep thinking: you only run the pelican once per model. Run the same model a few times and you get some different pelicans, so some of "this one is better" might just be which run you picked for it. Would love to see 8 runs per model side by side. I bet for two close models, the gap between runs is about as big as the gap between the models.

[simonw]

I've done versions in the past where I ran 3 and picked the best one. At some point I'd like to automate that with an LLM-as-a-judge (from the same model family) picking the "best" one to move forth in the competition.

I built a whole ELO scoring mechanism a while back, described here: https://simonwillison.net/2025/Jun/6/six-months-in-llms/#ai-...

I probably should spend some time on this now, even though the benchmark itself is feeling a bit stale. There's still a lot of demand for a gallery!

[not_a_bot_4sho]

If you're not doing *at least* say 100 iterations (thousands are preferred!!), you do not have enough data to draw any stable conclusions.

Interestingly enough, using an LLM-as-judge is a great way to approach things like this at scale but you do need to invest in some Cohen's Kappa or Fleiss' Kappa understanding which means putting a human in the driver seat to evaluate the effectiveness of your non-human judge. Absent of that, it's just another case of human-centipede but with LLMs.

[simonw]

I'm not sure there's any level of iterations that could result in a credible decision that model A clearly draws a better pelican riding a bicycle than model B.

What does "better" even mean there?

[not_a_bot_4sho]

(I came to delete but was too late. So edits are in.)

Wow, that's a stark take. I suppose I'm biased towards a scientific viewpoint. All the best.