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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!
Discussion (17 Comments)Read Original on HackerNews
Honestly, in my opinion, GPT-5.5 Codex doesn't just crush Claude Code—it's writing code at a level so advanced that I sometimes struggle to even fully comprehend it. Even when navigating fairly massive codebases spanning four different languages and regions (US, China, Korea, and Japan), Codex's performance is simply overwhelming.
How would we even go about properly measuring and benchmarking the Elo for autonomous agents like this?
the decays are just more capable other models entering the population, making all prior models lose more frequently
I hope to see the other labs can bring back competition soon!
That sounded like a press bulletin, so just to let you clarify yourself: Does that mean you may switch to lightly quantized models?
I am willing to bet large amounts of money that OpenAI would never release a model served as fully BF16 in the year of our lord 2026. That would be insane operationally. They're almost certainly doing QAT to FP4 for FFN, and a similar or slightly larger quant for attention tensors.
I know this might be a question that's impossible for you to answer, but if you can shed any light to this matter, I'd be grateful as I am doing an analysis over what AI solutions that can be suitable for my organisation.
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.
Thank you, I just looked at the chart and said to myself: ELO? YOLO!
That Elo ranking is also called chess ranking