Though one thing I've heard is that the base model is the one with the various possibilities for patterns, and then the reasoning takes advantage of those vs ne

Though one thing I've heard is that the base model is the one with the various possibilities for patterns, and then the reasoning takes advantage of those vs necessarily creating something new in that additional training. So even if it can't access that additional data without the reasoning, it doesn't mean it isn't in there. I think if it was JUST "how persistent is it at reasoning", they wouldn't have bothered to make Mythos a bigger model. It costs them more to run and probably puts a bunch of strain on how much compute they have available for other users and tasks. They have a lot of incentive to not use a larger model if they can get away with it. In the end, maybe "cost per task" is the best measure of "intelligence"? That takes into account raw size, but also how efficient they are with tokens. Like Sonnet 5 being actually more expensive than Opus 4.8 at various various benchmark tasks, showed it was very persistent but not super smart. If you just point it at easy tasks, it is probably cheaper, but hard ones you shouldn't bother because it isn't worth it even if it eventually gets there. Also, maybe this is too tautological to even mention but.. I have to wonder how much the labs even care and test for how well models do without reasoning turned on anymore? If almost all the training has reasoning turned on, probably have access to external tools, etc it is a bit hard to say how much it proves that they are dumb if they don't do well without it. As with all of AI, the amount of real "generalization" can be hard to suss out. But again, I think the real measure is how much can the model actually do, and how much does it cost. If a model can do something that couldn't be done before, even with the old model trying to brute force it, I think that still counts for some sort of intelligence in a practical sense anyway.