But did you test Muse Glimmer? IMO it is really worth comparing the two, at least. I really find it interesting (and while it is slower at outputting tokens, it solves problems better and usually even faster than the 3.6 35B MoE).
So far it happens to be the only one I've put code into production from (though I have done loads of useful self-teaching research with the Qwen models and I am grateful for them)
It is a good model but Qwen (at least for the things I use it for) edges just ahead, it seems much better at the “rip this apart, suggest improvements, touch nothing” use case where I can use it as a second set of eyes, I don’t agree with all its suggestions but it catches enough to be worth running while I grab coffee, it also seems to follow instructions better in terms of outputting more what I asked for than what it thinks I asked for.
Qwen is the only local model that said in its thinking “I think the user is pushing me to see if I’ll suggest something even though I have nothing to suggest, I should just say that” and then did, caught me off guard, they didn’t do that so readily 6mths ago.
The ISTA version is also comfortably able to fit on a 7900XTX with a good amount of space left for context and is decently fast given the AMD cards are not as fast as nvidia cards of same era/rough price, didn’t buy it for AI but it’s surprisingly capable mostly because 24GB at 960GB/s is still a lot of bandwidth compared to everything but nvidia cards.
I have a test where I ask the model to ask me any followup questions it needs, and Qwen 3.8 27B is the only one I have seen that won’t routinely take this as a prompt to just ask questions regardless. Muse Glimmer sometimes decides it has what it needs and has no need to ask; Qwen will generally just conclude it doesn’t need any more information. And sometimes it will ask questions with sensible defaults that I can accept collectively with a single answer.
However, when given the prompt to search if they need to, both of them will search when they don’t need to.