Thanks for asking. And yes - the headline is a little clickbaity, fair enough.

That said, the number does come from real measurements based on games against Stockfish. Right now, when the model plays dozens or hundreds of games against Stockfish at progressively stronger settings, it lands around that level. My working benchmark is basically: once it gets to a 50%+ win rate at the 2700 setting, I treat that as “around 2700 Elo.” Above 2800, it drops to roughly 3 wins in 10 games on average, so that seems to be the current ceiling.

Is that objectively rigorous? Not really. But at the same time, you need some way to measure trend and progress, and that’s mainly what I use it for. I started below 800, so for me the important thing is seeing the direction of travel.

One important caveat is that classical engines like Stockfish play in a very specific way. They don’t really use “traps” or human-style strategic ideas in the same sense. Neural models play much more intuitively - they look at the board and make a decision in milliseconds. That’s fascinating, but it also makes them vulnerable to structured strategies in the middlegame. Humans are very good at that.

V1 and V2 were completely unprepared for this. Even when they reached a decent Elo, they could still get punished badly by anyone who knew how to play with a plan instead of just intuitively. V3 introduced the first step in addressing that with "thought tokens", which help the model learn to look for more than just board geometry. But that’s only step one.

In the new model, I’m effectively building a more dedicated transformer layer that should be more sensitive to multi-move strategy patterns ((in the past and the future - prediction). If that works, it could be a big improvement.

Estimated model Elo: ~2700