This is a clean carve. Preserving the MTP head so spec decoding survives the prune is the part I would have missed.

Two things I would want to measure next.

Pruning by average competence has a tail your benchmarks cannot see. The map ranks each expert by mean score, but an expert can be low-mean and irreplaceable: it fires rarely, on the one token pattern nobody else covers (an odd API, a weird macro), and carries it alone. Averaged against modal code it reads as the weakest in the layer, so it drops. HumanEval 0.97 never notices, because HumanEval is modal code. The specialist lived in the tail, and a mean cannot hold a tail. Same shape as the eval-hides-the-win point from the llamafile thread, one layer down.

Second, the router is the tell. You had to widen top-8 to top-10, and that is not a free knob. The router learned its distribution against 256 experts. Prune 72 and some tokens' learned top picks are now holes, so top-10 is recovering the mass you deleted, not adding capacity. Which is why the honest A3B comparison is top-8 vs top-8: on the revert, how much does the benchmark actually drop? If top-8 holds, the carve was clean and top-10 is polish. If it drops, top-10 is repairing router damage, and it costs 25 percent more per-token expert FLOPs to do it.

So: what does the top-8 revert score?