While language models plausibly are trained with comparable amounts of FLOP to humans today here are some differences:

• Humans process much less data
• Humans spend much more compute per datapoint
• Human data includes them taking actions and the results of those actions, language model pretraining data much less so.

These might explain some of the strengths/weaknesses of language models

• LMs know many more things than humans, but often in shallower ways.
• LMs seem less sample-efficient than humans (less compute per datapoint and they haven't been very optimized for sample-efficiency yet)
• LMs are worse at taking actions over time than humans.