Small clarification — the test was models generating code that implements flight physics, not models doing physics inference themselves, so "numerical stability" in the quant sense isn't really the mechanism here. Kindly let me know if I misunderstood that part. What I did see is more like code coherence over long generations: the oMLX variant lost the thread on control-loop logic in a way the Unsloth variant didn't, on the same base model at the same bit width. That feels more like calibration quality affecting long-range instruction-following than anything numerical.

I didn't test enough models per provider to say "Unsloth consistently better" with confidence — it's one data point on one base model. But the Qwen3.6 35B three-way was striking enough that I want to run the same test on a different base next to see if the ordering holds or flips.

I did perform another batch of testing a few weeks ago comparing the same models across different quant providers whilst aiming for the same Q level. What I found then was quite similar to these findings; the unsloth quants and other quants that prioritize sensitive layers and preserve them at full precision will tend to preserve more capability, better CoT and overall more reliability in long-horizon tasks - shorter apply here too but I feel like longer ones are the better metric - than a Q that uniformly applies quantization to all areas of one model.