Product moves Aug 17, 2026

This week · event date 08-16

The default setting is the real behaviour: a good laptop-class model treats "draw a circle" as a major commission.

Simon Willison (see Named commentary) tested Alibaba's newly released Qwen 3.8 27B — Apache 2 licence, open weights, vision-capable, about 17GB once quantised to lower precision, which fits a sensibly specified laptop — and rates it among the best local models available on laptop-class hardware. But it ships with reasoning effort set to the highest of its four levels. On the same prompt — the vector drawing of a pelican riding a bicycle he has used as a fixed test for years — reasoning on took 21 minutes and burned 22,276 reasoning tokens, while reasoning off took 137 seconds, a difference of roughly 9.2x. ⚠️ The two runs produced output of similar length (3,223 tokens against 3,715); the whole difference sits in the reasoning segment. The extreme case came from the prompt "draw an svg of a circle," where the reasoning trace opens by talking itself into "concentric guide circles (like a compass/geometry drawing), tick marks, a soft gradient fill on the main circle, restrained ambient motion (a slowly rotating dashed ring, pulsing glow)." A second trap misleads people: the local runner he used defaults to a context limit of 8,192 tokens, which the thinking alone exhausts, and raising it to the full 262,144 made the problem disappear (the full write-up). Two things to do today: when you run a local model, turn the reasoning effort down and raise the context limit to its maximum. Those two defaults decide the behaviour you actually get — four documented levels do not mean users receive four levels. ⚠️ This is one developer's first-hand test rather than a benchmark run. Qwen's own reported numbers show gains over both the previous generation and its own closed-weight model. ⚠️ Those are vendor-reported, with no independent verification yet.