Qwen3 is substantially better in my local testing. As in, adheres to the prompt better (pretty much exactly for the 32B parameter variant, very impressive) and is more organic sounding.
In simplebench gpt-oss (120 bn) flopped hard so it doesn't appear particularly good at logical puzzles either.
So presumably, this comes down to...
- training technique or data
- dimension
- lower number of large experts vs higher number of small experts
Qwen3 32B is a dense model, it uses all its parameters all the time. GPT OSS 20B is a sparse MoE model. This means it only uses a fraction (3.6B) at a time. It’s a tradeoff that makes it faster to run than a dense 20B model and much smarter than a 3.6B one.
In practice the fairest comparison would be to a dense ~8B model. Qwen Coder 30B A3B is a good sparse comparison point as well.
Qwen Coder 30B is my main driver in this configuration and in my experience is quite capable. It runs at 80 tok/s on my M3 Max and I'm able to use it for about 30-50% of my coding tasks, the most menial ones. I am exploring ways to RL its approach to coding so it fits my style a bit more and it's a very exciting prospect whenever I manage to figure it out.
The missing link is autocomplete since Roo only solves the agent part. Continue.dev does a decent job at that but you really want to pair it with a high performance, large context model (so it fits multiple code sections + your recent changes + context about the repo and gives fast suggestions) and that doesn't seem feasible or enjoyable yet in a fully local setup.