Qwen3.5 beats Kimi K2.5 locally

This reads less like a model showdown than a reminder that local AI is about fit, not just intelligence. Qwen3.5 looks strongest when quantization and VRAM budget line up, but its longer reasoning can erase the real-time speed advantage, and the 262K context plus thinking mode help explain the token burn. The interesting part is not the toy prompt itself; it is that a 35B MoE model can be made usable on a 16GB card with aggressive quantization. The poster's edit is the real takeaway: local Qwen can be quicker per token, but longer reasoning often makes it a wash against Kimi K2.5 on end-to-end latency. The thread also suggests it is useful beyond demos, especially for long-context document generation and repeated workflow tasks where consistency matters more than benchmark theater, and stack choices like LM Studio, Vulkan, and quantization decide whether the setup feels practical or merely impressive.