Haven't tried GPT-OSS-20B yet — the MOE approach is interesting for keeping VRAM usage down while getting better reasoning. 85 t/s on a 3060 is impressive. I'll look into that.

I've been on Qwen3 8B mostly because it was "good enough" for the mechanical stages (scanning, scoring, dedup) and I didn't want to optimize the local model before validating the orchestration pattern itself. Now that the pipeline is proven, experimenting with the local model is the obvious next lever to pull.

The Qwen3 4B 2507 claim is interesting — if the quality holds for structured extraction tasks, halving the VRAM footprint would open up running two models concurrently or leaving more room for larger contexts. Worth testing.

Thanks for the pointers — this is exactly the kind of optimization I haven't had time to dig into yet.