I'm disappointed they didn't release a 27B dense model. I've been working with Qwen3.5-27B and Qwen3.5-35B-A3B locally, both in their native weights and the versions the community distilled from Opus 4.6 (Qwopus), and I have found I generally get higher quality outputs from the 27B dense model than the 35B-A3B MOE model. My basic conclusion was that MoE approach may be more memory efficient, but it requires a fairly large set of active parameters to match similarly sized dense models, as I was able to see better or comparable results from Qwen3.5-122B-A10B as I got from Qwen3.5-27B, however at a slower generation speed. I am certain that for frontier providers with massive compute that MoE represents a meaningful efficiency gain with similar quality, but for running models locally I still prefer medium sized dense models.
I'll give this a try, but I would be surprised if it outperforms Qwen3.5-27B.
Given that DeepSeek, GLM, Kimi etc have all released large open weight models, I am personally grateful that Qwen fills the mid/small sized model gap even if they keep their largest models to themselves. The only other major player in the mid/small sized space at this point is pretty much only Gemma.
I'm totally fine with that, frankly. I'm blessed with 128GB of Unified Memory to run local models, but that's still tiny in comparison the larger frontier models. I'd much rather get a full array of small and medium sized models, and building useful things within the limits of smaller models is more interesting to me anyway.
It's a given that the dense models with comparable size are better. I also proved that in my use case for those two Qwen 3.5 models.
The benchmarks show 3.6 is a bit better than 3.5. I should retry my task, but I don't have a lot of confidence. But it does sound like they worked on the right thing which is getting closer to the 27B performance.
I'll give this a try, but I would be surprised if it outperforms Qwen3.5-27B.