[zozbot234]

Good catch. If it's "too slow" even when ran in a state-of-the-art datacenter environment, this "Mythos" model is most closely comparable to the "Deep Research" modes for GPT and Gemini, which Claude formerly lacked any direct equivalent for.

[stalfie]

I don't think that's what's being hinted at. The system card seems to say that the model is both token efficient and slow in practice. Deep research modes generally work by having many subagents/large token spend. So this more likely the fact that each token just takes longer to produce, which would be because the model is simply much larger.

By epoch AIs datacenter tracking methods, anthropic has had access to the largest amount of contiguous compute since late last year. So this might simply be the end result result of being the first to have the capacity to conduct a training run of this size. Or the first seemingly successful one at any rate.

[zozbot234]

"Slow and token-efficient" could be achieved quite trivially by taking an existing large MoE model and increasing the amount of active experts per layer, thus decreasing sparsity. The broader point is that to end users, Mythos behaves just like Deep Research: having it be "more token efficient" compared to running swarms of subagents is not something that impacts them directly.