Hey guys, I tested both of them and feel Qwen3codernext is more useful, whats your thoughts? thanks for your sharing.
I started running some benchmarks, and Qwen 3.6 seems to come out on top on those:
I haven’t done much real-world testing though, because it’s really hard to compare that way and I wanted to answer the same question you had.
I’m currently running the 27B dense model with dflash to see what different it makes to the speed (in theory the quality should be the same). I was surprised that in the benchmarks I’ve run, the dense model hasn’t actually been as much slower as I’d expected (presumably it’s more efficient and getting the answer and doesn’t go in loops or fail tool calls as often).
I find myself using one, then finding it is getting stuck in a rut, and then swapping out for the other to fix a problem, then swapping back again once that particular problem is fixed. It is increasingly looking like I need a cluster to run more than one at a time, and then use a smaller model to act as intermediary to the other coding models…
Problem is - things are moving so fast, new models seem to arrive every few days, and runtime recipes with different options seem to float around every few hours - it is difficult to keep track, especially as I’m pretty new to this.
I asked this question to myself multiple times. For my coding workflows, both work well and perform almost identically. I found myself going back and forth between these two and 3.5-122B-A10B (with all the optimizations from @Albond and @whpthomas’ recipes it performs very well.
Sometimes “coder-next” get stuck in a few things and 122B fixes it at once.
Sometimes 122B loops itself and 35B-A3B-FP8 solves that at once.
I guess the real answer is “its depends” :) I’m still defaulting back to 35B mosts of the times. Peforms very well and doesn’t make ugly mistakes too often.