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Yeah, the "thinking" process itself also counts as output tokens. When you use a reasoning model, it's basically writing a long monologue about how it's going to solve your problem and then immediately throwing it away at the end. (Different providers have different policies about whether you're allowed to see this monologue, but it often significantly exceeds the length of the actual code or whatever that the AI is writing.)
So, I'm kind of clueless about this, but are reasoning models are actually different models, as in different neural net weights?
Like, do you get a reasoning model by running a single-pass model in a loop where you feed it prompts like: "first, understand the problem and make a plan for solving it, formatted like this", then "here's the plan you thought up before, try to execute point 1 now", and so on?
Or do you need a different model specially-trained for this kind of thing and it's a big secret black box how it all works?
Yes. Generally, reasoning models are trained to use special "start thinking" and "stop thinking" tokens, and to generate a specific kind of monologue in between those tokens. Similar to how RLHF biases models towards producing text that's appealing to human readers, reasoning models use techniques like RLVR to bias towards generating monologues that end in a correct solution to a problem.
Many reasoning models are trained in a way that lets you disable the reasoning by forcing them to never generate the "start thinking" token -- Claude Opus 4.8 probably uses the same weights regardless of whether you enable or disable thinking, for example -- but their weights are different from models that were never trained for reasoning in the first place.
With that being said, people used to use "chain of thought prompting" to get a similar kind of result out of regular LLMs. (I think reasoning models basically got started when AI companies saw the early success of chain-of-thought prompting and started baking it in at the training stage.)
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