i spent this week trying to understand where reasoning actually happens in a model.

it started with a post on my timeline. reflection and hugging face were giving $20k to people building with Beam, reflection's new 501B open model. i had never gone this deep into how a model thinks, so i used it as an excuse to learn.

demo: https://huggingface.co/spaces/PengoSword123/beamdial

data: https://huggingface.co/datasets/PengoSword123/beamdial-v0.1

code: https://github.com/CoderRahul01/beamdial

the question

Beam has a setting called reasoning_effort. 5 levels, low to max. higher means the model thinks longer before it answers, and those thinking tokens are billed like any other output.

most developers set it once and forget it. usually on max.

a smart model should think hard on hard problems and answer fast on easy ones. you shouldn't have to guess.

what i measured

-> 410 problems with known answers. school math, competition math, olympiad problems, python coding

-> every problem at all 5 levels.…