I kicked some tired today and put it to do some coding work with the 26B-A4B. The model loaded fast, inferred > 50 tokens per second, and directly run with my default speculative decoding setup that uses no LLM, just generates long sequences of tokens from the existing context as predictions. That worked, and at times the model ran 100 tokens per second when it was just echoing the code files without edits, so it was pleasantly fast.

Then I looked into what it was actually doing in Kilo Code. I had told it to make some HTML template edits, and I had the files already open in the editor which should have told the model the paths to the files I wanted to edit -- this always works with Qwen3.5 -- but for some reason it just didn't pick up the hint. This thing started looking for the files, had discovered some compiled TypeScript artifacts, which it then read in chunks because they are large, it found all sorts of crap inside, read it and then seemed to loop in reasoning and got stuck.

I guess the poor bastard just confused itself from reading all that minimized JavaScript.

I think the non-MoE model might be fine, and I can't rule out inference problems since this is the early days. Thus far the experience is a step-down, especially as Gemma-4 did not come in some suitable 120B-A8B type size which could have been competitive against Qwen3.5's similarly sized offering which to date remains the most practical model I can run on a Ryzen AI Max. Gemma 4 may well feel like going back 6 months and again having to babysit these models in agentic tasks, which is not what I'm hoping to have to do ever again.