My concern is that reasoning could involve some sequential steps that instant models don't.
Not sure if modern models "think" only by outputting <thinking> blocks, or there is a more complex mechanism at play.
Not sure if modern models "think" only by outputting <thinking> blocks, or there is a more complex mechanism at play.
That's pretty much it - a small refinement to "Chain of Thought" prompting, where you tell the model explicitly in the prompt to "Think step by step" or similar, so it writes out more steps before giving a final answer, potentially catching some errors. The "thinking" models are tuned to do that without being prompted to, and to output the "thinking" markers around it, so they can be hidden from the user.
I am curious what the drop in thoughput is for multi-turn answers, instead of one-shot. More in line with the current "agentic" use-cases.
Probably, the usual initial suspects for “what makes computation slow” will become a focus point that needs to be optimized again: file access, network, etc.