What is the main benefit of these 'reasoning' models?

Reasoning models pause to work through a problem in intermediate steps before producing a final answer, rather than generating a response in one immediate pass the way earlier chatbots typically did. By spending extra computing effort at the moment you ask a question, generating an internal scratchpad of reasoning, these models can catch mistakes, explore multiple approaches, and handle multi-step problems, noticeably improving performance on hard math, logic puzzles, and complex coding tasks.

These models still require an internet connection when run through a cloud service, so that option misunderstands how they're deployed. They also don't use less memory than older models; the extra step-by-step reasoning tends to use more computing resources per answer, since generating that hidden reasoning takes additional processing time and power.

This shows that simply making a model bigger isn't the only path to better performance; letting a model "think longer" at the moment you ask can sometimes match or beat the gains from training an even larger model from scratch.