It feels clear to me that smaller models are moving parameter-level thinking into inference time. i.e. they use more tokens to perform computation because they don't perform it in the model itself. There's some conservation of difficulty for tasks and you either do the computation in the model or you do it in the looped iterations of the model.

This is why the massive tok/s you see on many small MoE never manifest as worthwhile outcomes. Sonnet 5.5 can be faster on tasks than DeepSeek 4 Flash Vision simply because it gets there faster.

Indeed — the output token speed is showy but not that useful as a metric.

Muse Glimmer (dense 30B), in practice, can get to better answers more quickly than Qwen 3.6 35B-A3B.

It just appears to be slower word by word.