Interesting, thanks for highlighting! Did not pick up on that. Re:"leading", tho:
Effectiveness in this task environment is well beyond the specific model involved, no? Plus they'd be fools (IMHO) to only use one size of model for each step in a research task -- sure, o3 might be an advantage when synthesizing a final answer or choosing between conflicting sources, but there are many, many steps required to get to that point.
I don't believe we have any indication that the big offerings (claude.ai, Gemini, operator, tasks, canvas, chatgpt) use multiple models in one call (other than for different modalities like having Gemini create an image). It seems to actually be very difficult technically and I'm curious as to why.
I wonder how much of an impact our being still so early in the productization phase of this all is. Like it takes a ton of work and training and coordination to get multiple models synced up into an offering and I think the companies are still optimizing for getting new ideas out there rather truly optimizing them.
Effectiveness in this task environment is well beyond the specific model involved, no? Plus they'd be fools (IMHO) to only use one size of model for each step in a research task -- sure, o3 might be an advantage when synthesizing a final answer or choosing between conflicting sources, but there are many, many steps required to get to that point.