Apparently this is not very far from how things looked like 2 years ago. KataGo’s biggest models are less than 1 GB in size. Transformer support was just added in the latest major release, stating that transformers are “generally much stronger given the same compute cost” compared to the current model architecture. So probably a correctly trained 1B param dense transformer is stronger.
The biggest LLMs of 2026 (Mythos 5, probably Astra) likely have about 1T active params (and 10T total). It’s a miracle of narrow AI that KataGo is still so strong.
I’m not sure how to think about the number of parameters required to be good at Go compared to the number of parameters in frontier LLMs. It seems not thaaaat surprising to me if much fewer parameters are required to be vastly superhuman in Go. In the last domain that I worked in, AI for weather forecasting, it’s possible to outperform the best physics-based models with O(100M) parameters.
The first comment on the Reddit post you linked quotes rating estimates that put Shin Jin-seo as comparable to AlphaGo Lee (AlphaGo Lee 3040, Shin JinSeo 3060). But I have been under the impression that KataGo even in the first few years after it was created was already vastly stronger than AlphaGo Lee. (And it’s been getting stronger this whole time up to 2026; the training run is still ongoing.) I couldn’t find direct comparisons very easily, but my logic chain goes something like:
AlphaGo Lee beat Lee Sedol 4-1 in 2016
One year later, AlphaGo Zero was already beating AlphaGo Lee 100-0
In 2020, the creator of KataGo commented that KataGo was “very likely” to be “much stronger than AlphaGo Lee” and also likely stronger than AlphaGo Zero
Apparently this is not very far from how things looked like 2 years ago. KataGo’s biggest models are less than 1 GB in size. Transformer support was just added in the latest major release, stating that transformers are “generally much stronger given the same compute cost” compared to the current model architecture. So probably a correctly trained 1B param dense transformer is stronger.
The biggest LLMs of 2026 (Mythos 5, probably Astra) likely have about 1T active params (and 10T total). It’s a miracle of narrow AI that KataGo is still so strong.
I’m not sure how to think about the number of parameters required to be good at Go compared to the number of parameters in frontier LLMs. It seems not thaaaat surprising to me if much fewer parameters are required to be vastly superhuman in Go. In the last domain that I worked in, AI for weather forecasting, it’s possible to outperform the best physics-based models with O(100M) parameters.
The first comment on the Reddit post you linked quotes rating estimates that put Shin Jin-seo as comparable to AlphaGo Lee (AlphaGo Lee 3040, Shin JinSeo 3060). But I have been under the impression that KataGo even in the first few years after it was created was already vastly stronger than AlphaGo Lee. (And it’s been getting stronger this whole time up to 2026; the training run is still ongoing.) I couldn’t find direct comparisons very easily, but my logic chain goes something like:
AlphaGo Lee beat Lee Sedol 4-1 in 2016
One year later, AlphaGo Zero was already beating AlphaGo Lee 100-0
In 2020, the creator of KataGo commented that KataGo was “very likely” to be “much stronger than AlphaGo Lee” and also likely stronger than AlphaGo Zero
So yeah I feel like I’m still confused?