Seems like the industry is moving further towards having low-latency/high-speed models for direct interaction, and having slow, long thinking models for longer tasks / deeper thinking.
Quick/Instant LLMs for human use (think UI). Slow, deep thinking LLMs for autonomous agents.
You always want faster feedback. If not a human leveraging the fast cycles, another automated system (eg CI).
Slow, deep tasks are mostly for flashy one-shot demos that have little to no practical use in the real world.
I mean, yes, one always does want faster feedback - cannot argue with that!
But some of the longer stuff - automating kernel fusion, etc, are just hard problems. And a small model - or even most bigger ones, will not get the direction right…
From my experience, larger models also don't get the direction right a surprising amount of times. You just take more time to notice when it happens, or start to be defensive (over-specing) to account for the longer waits. Even the most simple task can appear "hard" with that over spec'd approach (like building a react app).
Iterating with a faster model is, from my perspective, the superior approach. Doesn't matter the task complexity, the quick feedback more than compensates for it.
Are they really thinking or are they sprinkling them with Sleep(x)?