I especially wonder about the quality of N big models in a delegation harness against M smaller models in a delegation harness with N and M tuned to use the same amount of total compute. (So a few agents of big models against a lot of big models). I wouldn't be surprised if the advantages of delegation and the corresponding compression of context outweighs the lower quality of the smaller models.

Or put differently a wide exploration of long chains of obvious insights might be more valuable than a more narrow exploration of shorter chains of deeper insights. But perhaps that is a wrong sense of the difference between a small model and a large model.