The fact that these reasoning models may compute for extended durations, using exponentially more compute for linear performance gains (says OpenAI), resulting in outputs that while better are not necessarily any longer (more tokens) than before, all point to a different architecture - some type of iterative calling of the underlying model (essentially a reasoning agent using the underlying model).
A plain LLM does not use variable compute - it is a fixed number of transformer layers, a fixed amount of compute for every token generated.
