Arena AI Model ELO History
mayerwin.github.ioHi HN,
I built a live tracker to visualize the lifecycle and performance changes of flagship AI models.
We've all experienced the phenomenon where a flagship model feels amazing at launch, but weeks later, it suddenly feels a bit off. I wanted to see if this was just a feeling or a measurable reality, so I built a dashboard to track historical ELO ratings from Arena AI.
Instead of a massive spaghetti chart of every single model variant, the logic plots exactly ONE continuous curve per major AI lab. It dynamically tracks their highest-rated flagship model over time, which makes both the sudden generational jumps and the slow performance decays much easier to see. It took quite a lot of iterations to get the chart to look nice on mobile as well. Optional dark mode included.
However, I have a specific data blindspot that I'm hoping this community might have insights on.
Arena AI largely relies on testing API endpoints. But as we know, consumer chat UIs often layer on heavy system prompts, safety wrappers, or silently switch to heavily quantized models under high load to save compute. API benchmarks don't fully capture this "nerfing" that everyday web users experience.
Does anyone know of any historical ELO or evaluation datasets that specifically scrape or test outputs from the consumer web UIs rather than raw APIs?
I'd love to integrate that data for a more accurate picture of the consumer experience. The project is open-source (repo link in the footer), so I'd appreciate any feedback, or pointers to datasets!
For what it's worth, I work at OpenAI and I can guarantee you that we don't switch to heavily quantized models or otherwise nerf them when we're under high load.
> the slow performance decays
the decays are just more capable other models entering the population, making all prior models lose more frequently
FYI, Elo isn't an acronym - it's a person's name. No need to capitalize it as ELO.
You’re right: https://en.wikipedia.org/wiki/Elo_rating_system
The ELO rating system measures relative performance to the other models. As the other models improve or rather newer better models enter the list, the ELO score of a given existing model will tend to decrease even though there might be no changes whatsoever to the model or its system prompt.
You can't use ELO scores to measure decay of a models performance in absolute terms. For that you need a fixed harness running over a fixed set of tests.