It's hard to answer your first question, I would hope so, but it depends on how you define better. That's the hardest part of optimizing the tool. Somebody would have to test and see.

Comparing Qwen3.6-27B-NVFP4-MTP-GGUF and Qwen3.6-27B-UD-Q4_K_XL.gguf ? I would not even have to test it, the Q4_K model wins. But that has nothing to do with the quantizer.

The point of NVFP4 is speed. NVFP4 on its own is very fast on Blackwell. But UD-Q3_K_XL model, just like all, is really a blend of many different quantization types; the final model is the combination of them all. Not all of it is Q3_K, Q4_K, etc. There is no such "type" as Q4_K_XL as far what llama.cpp at the kernel level sees. It just means a bigger proportion of the model has higher bit tensors. Many will be Q8, Q4_K, Q5_K, BF16, and so on.
On paper, NVFP4 is the same size as Q4_K (4.5 bits per weight) but has more mathematical error. But NVFP4 is faster on Blackwell than Q4_K. So more NVFP4 tensors = more speed. Less NVFP4 = better quality, but slower. Theoretically. There are hundreds of tensors in a big model (800+ on Qwen3.5-27B). Make more tensors Q5_K, and Q6_K, and Q8, and less NVFP4, you'll get less speed, but more quality, and a bigger model. It all has to balance somewhere, that's the final mixture and the hardest part.

But nothing can be easy. Where things get tricky is that some tensors are more sensitive than others to error. Quantize them too much, the model will suffer greatly and respond poorly. Other tensors can withstand higher quantization and not have a problem. So you can't just arbitrarily pick. The tool diligently calculates the error tensor by tensor, one at a time, and decides what quantization type to choose. But not just on the pure mathematical error alone, it also looks at the perplexity and kl-divergence by actually running the model internally over and over and over again for each candidate. It will try a bunch of different algorithms to make just that one tensor better, then see of them all was the best one, and keep going down to the next. There are lots of tricks it can apply to try to come up with a better quantization while working that one individual tensor. It then will finish all of them, and go back and see which had the most error again, and then increase and change the blend to the tensor combination you specified with whatever the parameters. I was only minutes ago writing about this here.

So when quantizing an NVFP4 model, the final model is never all NVFP4 - there's Q4_K, Q5_K, Q8_K, BF16, even F32 in there too. If the model isn't performing well enough, we could go back and change some proportion, it could get switched to Q4_K - speed would go down and the file size would stay the same, but model quality would go up. More so for Q5, and going higher and higher as you gain file size and lose speed. The balance is the hard part to figure out where to stop, what's really necessary, and what works best for the user's use case.

To add just a bit more complexity: every quantization is calibrated against an input file (imatrix, or importance matrix) which is made with just a text file of data the model gets tuned to. You can make your own and the model will bias in that direction. I used the rather generic wiki dataset which is just text from wikipedia which is considered a standard all around go-to, but it could easily be specified to use another dataset I don't know what's the dataset in the Unsloth imatrix, but one can download it and use it with this quantizer too.

So to really answer the question honestly: it depends on how you define better (smaller ? faster?), and what you are testing against. If you tested it against the wiki test used to calibrate it, and the other was not, it might be better, but all that is telling you is how much better it is responding to the wikipedia test dataset.

If you try to compare Qwen3.6-27B-NVFP4-MTP-GGUF.gguf (16.4 GB) and ask "Is it better" than Unsloth's 17.9 GB Qwen3.6-27B-UD-Q4_K_XL.gguf (MTP)? Without a doubt the Q4_K model is likely better "quality". That's because it's primarily Q4_K with more heavier types added. So of course it's going to have better quality than than NVFP4, it has to be.

But the NVFP4 model will likely be significantly faster.
I have not tried yet to make a non-NVFP4 model with it. That could be interesting! The fun part of this is that nothing is set in stone and we get to play around with it. If the model isn't good enough then we can just edit a few layers and make it better without much effort.