For coding, qwen 3.6 35b a3b solved 11/98 of the Power Ranking tasks (best-of-two), compared to 10/98 for the same size qwen 3.5. So it's at best very slightly improved and not at all in the class of qwen 3.5 27b dense (26 solved) let alone opus (95/98 solved, for 4.6).

That's not surprising; Opus & Sonnet have been regressing on many non-coding tasks since about the 4.1 release in our testing

Going to have to disagree on the backup test. Opus flamingo is actually on the pedals and seat with functional spokes and beak. In terms of adherence to physical reality Qwen is completely off. To me it's a little puzzling that someone would prefer the Qwen output.

I'd say the example actually does (vaguely) suggest that Qwen might be overfitting to the Pelican.

I understand the 'fun factor' but at this point I really wonder what this pelican still proofs ? I mean, providers certainly could have adapted for it if they wanted, and if you want to test how well a model adapts to potential out of distribution contexts, it might be more worthwhile to mix different animals with different activity types (a whale on a skateboard) than always the same.

I'm really curious about what competes with Claude Code to drive a local LLM like Qwen 3.6?

I've been using Qwen3.5-35B-A3B for a bit via open code and oMLX on M5 Max with 128Gb of RAM and I have to say it's impressively good for a model of that size. I've seen a huge jump in the quality of the tool calls and how well it handles the agentic workflow.

How about switching to MechaStalin on a tricycle? It gets kind of boring.