Alibaba’s Qwen3.8 27B (Apache 2.0, released Aug 14) scored 52 on the Artificial Analysis Intelligence Index — #1 in the small open-weights category (4B–40B), beating every medium model (40B–150B), and tying DeepSeek V4 Flash 0731 (ranked #5 in the >150B large model category). That’s a remarkable jump from Qwen3.6 27B’s 38. The catch? It defaults to xhigh reasoning effort, which Simon Willison documented in hilarious detail: the model spent 21 minutes and 22,276 reasoning tokens producing an SVG of a pelican on a bicycle, and several minutes generating an animated geometric circle from the prompt “draw an svg of a circle” — beautiful output, but entirely not what was asked. The model generated 160M output tokens during the AA evaluation vs a 43M median, making it nearly 4× as verbose as comparable models. The recommendation: run on low or no reasoning for everyday use. The model itself is genuinely excellent — 17GB Q4_K_M quant available for LM Studio, fits on consumer hardware — but that default is a trap.
Qwen3.8 27B scores 52 on Artificial Analysis — matching 671B-class models at 27B parameters
Qwen3.8 27B scores 52 on Artificial Analysis — beating all medium models and tying DeepSeek V4 Flash. But its default xhigh reasoning effort makes it spectacularly wasteful out of the box.