This confirms a theory I have to explain the minimal loss in quality when using lower quants (I use IQ3_XXS with an 8-bit KV cache) and the XHIGH (default) thinking level.

It's well-known that while quantization affects the sampling probability distribution (given the same context, which next token is the most probable), Qwen 3.8 27b seems to offset that by just thinking more and as a result eventually finishing the task (benchmark or otherwise).

So as long as the thinking (albeit longer) is sound, this leads to the same success rate (as shown in the article) but potentially at the cost of more tokens and hence more time.

I think it'll be further useful to chart each quantization's used tokens as well, in addition to the success rate.

Thanks for doing and sharing the research!

Not for me. As stated elsewhere, even Q5 (!) seems to be enough to kill the model’s ability to solve a particular problem in reasonable time. But that might just be right at the edge of what the model can do in the first place.

I have another personal benchmark problem (of a very different nature) that Qwen3.8-27B usually can’t solve at all, while Opus and GLM-5.3-Flash solve it consistently and very beautifully.

As they say, time is money.

In the age of the rampocalypse, the peasants may not have a choice between the two.. time it is!

I personally think thinking is basically variable but rate precision. If you are in a 4bit mode but need 2x as many tokens you're just doing fp8 with hoops( of course 4bit multiply is faster)

If it digs itself into a hole, try low or medium. In the rust coding benchmarks (on my machine) it did better on low and medium because xhigh never finished.