many people ask when to use a big model (like sol/fable) at low reasoning effort, vs a small model (like luna/sonnet) at high reasoning effort i deliberately forced myself to use all the permutations a lot over the last couple of weeks to build intuition, and i realized the difference is "wisdom" vs "diligence" bigger models are "wiser" they have seen a lot. they remember a lot. they have a lot of expertise across different domains. they have better intuition, can connects the dots, and come up with creative, inspired ideas reasoning effort makes a model more "diligent" it'll assess each option, think through consequences, and figure out edge cases etc more thoroughly if there are 100 paths ahead, diligence makes the model assess every single one without a miss, but it will not make the model realize maybe the best one is to take none of the 100 paths and instead dig a tunnel "wisdom" and "diligence" are orthogonal. and now i get why the models are launched the way they were, and not just a single fable level model with 12 different reasoning levels we're all misled by the way we've been plotting the models with benchmark scores, which are fundamentally flawed because they use a single dimension to measure the model's capability, making us think of model size and reasoning effort as being fungible with each other, while in fact "wisdom" and "diligence" needs to be measured separately i hope the evals eventually catch up and address this. until then, here's my recommendation for how to choose - - if the problem you are trying to solve is something you think requires a genius, use a bigger model - if the problem you are trying to solve is something you think requires a pen and lots of paper, use higher reasoning effort - if it requires a genius sitting down with a pen and lots of paper, tune up both