LocalLLaMA users cascade Qwen 3.6 MoE, dense models

The Qwen 3.6 release highlights a shift toward orchestration patterns as a workaround for the inherent "laziness" of sparse MoE models. While the 35B-A3B MoE model offers significant speedups with only 3B active parameters, its sparse nature can lead to logical lapses that the 27B dense model avoids. Users are adapting tools like Roo Code and subagent scripts to automate these fallbacks, essentially creating a local "Small Model, Large Model" hierarchy for agentic tasks. "Thinking Preservation" in Qwen 3.6 is a key feature for maintaining coherence during these cross-model handoffs, though model self-awareness remains a primary bottleneck. This pattern suggests that local developer workflows are moving toward complex orchestration layers rather than relying on a single "jack-of-all-trades" model.