As we saw in previous articles, Qwen3.6 are very good LLMs for local AI.
On one side, Qwen3.6-35B-A3B is a sparse Mixture-of-Experts model with 35B total parameters, but only around 3B active parameters per token. On the other, Qwen3.6-27B is a dense model: smaller in total parameter count, but much heavier for each generated token.
In practice, the two models target slightly different users. The 35B-A3B is attractive if you want faster inference. The 27B is simpler: no router, no experts, and no MoE-specific surprises, but slower and more accurate.
Shortly after Qwen3.6 appeared, the community started releasing custom variants, including Qwopus3.6-35B-A3B, Qwopus3.6-27B, and Qwen3.6-28B-REAP.
They are all based on Qwen3.6, but they are not trying to solve the same problem. Qwopus focuses on reasoning style, answer structure, and distillation from stronger models. REAP focuses on reducing the size of the MoE by pruning experts.
This raises a practical question: should you use the original Qwen3.6 model, one of the Qwopus fine-tunes, or the smaller REAP version?
In this article, I compare these Qwen3.6 variants across several dimensions: how they were made, how much memory Qwen3.6-28B-REAP saves compared with the original Qwen3.6-35B-A3B, how token-efficient they are, and how accurate they are across different tasks and domains.
As we will see, customizing advanced models like Qwen3.6 is difficult. Accuracy alone is not enough. To properly adopt or validate a model, we also need to look at efficiency, failure modes, and behavior at scale.
The Custom Qwen3.6 Models Compared
Acknowledgements
Before turning to the analysis, which is largely negative for all these models, I would like to thank 0xSero and Jackrong for releasing them and for investing their time, and money, into exploring new recipes to make local AI better and more affordable. These models are extremely valuable for research purposes.