The key point here, I think, is not the 4-bit but the QAT — the model is trained with the objective of losing the least at 4-bit quantiZation (I am assuming it is literally about assigning numbers that quantize better).
Gemma 4 12B QAT is amazing - agents run very fast, and it's really very smart, at least in my agent's harness domain which is GNU software development - on par with frontiers like GPT Sol, DeepSeek, or Claude - Why to buy those expensive tokens if a local tiny model performs so well?
They’re exaggerating or have a very simple way of using these models. The Gemma 4 series, even at 31B, is nowhere near the frontier. They’re great models, but you will notice a huge difference for complex tasks.
The best local agentic coding experience I’ve had so far is Qwen3.6-27B with Pi.
> They’re great models, but you will notice a huge difference for complex tasks.
Yes and no. I think where frontier models really blow small models away is in how thorough they are in order to infer your intentions and how best to accomplish them. So you can tell Claude "change this code to make it do X", whereas a Qwen3.6-27B or Gemma4-31B can do the same, but you have to be a lot more thorough, i.e., "change this code to make it do X, but first, let me explain the concept of X as I see it and some notes about things to avoid or pay attention to while you're doing it." So for best success with small models you really need a big toolbelt of skills and MCPs.
I haven't dug into QAT deeply, better recovery is my understanding as well, and also that it is out of reach for most people because you have to train a model to back prop errors based on estimated error under quant.
Hopefully more of the lab releases are trained under QAT so we can all benefit.
The 12B QAT model is indeed sort of mindblowing.