I'm finding that the Mixture-of-Experts (MoE) models (Qwen 3.6-35B, and Nemotron 3.5 Lightning) are, well, terrible at this. They just couldn't get the job done at all. Went way off the rails. They are really fast though!
Whereas ~30B dense models (not MoE) are pretty decent. I tried Muse Glimmer, Gemma 4-31B, Qwen 3.6-27B, and Laguna XS[0]. They were all able to build a working collaborative whiteboard app, without any guidance (other than feeding back error logs to the model). I also asked each to then draw a monkey by calling the API of the whiteboard it has just built. Laguna drew random scribbles but the rest all managed to produce something monkey-like.
(Frontier models in comparison will write the app in one shot with no errors at all.)
Note that both Qwen 3.6 and Gemma 4 each have both MoE and dense variants. I find this very confusing, because e.g. ollama's model index typically only distinguishes variants by their size, but MoE vs. dense makes a huge difference in how they actually perform. IMO they should use a suffix, like Qwen 3.6-moe vs. Qwen 3.6-dense, or maybe Qwen 3.6-fast vs. Qwen 3.6-smart...
[0] EDIT: Turns out Laguna XS is MoE, I misunderstood. It performed similarly to the dense models. But maybe this explains why it couldn't write code and think about monkey shapes at the same time!