Reasoning Model
Definition
A class of language models, established since 2024/2025, that produce a longer internal chain of thought before the actual answer — visible or hidden — and deliver significantly better results on mathematical, planning and multi-step tasks than classical LLMs. 2026 representatives include the OpenAI o-series, Claude with Extended Thinking and the DeepSeek R1 line.
Noise — Signal
Reasoning models are advertised as "AI that finally understands what it's doing". What they actually do is produce structured token sequences before the answer. That noticeably lifts quality on certain tasks but drives inference cost and latency by a factor of five to twenty. For most productive applications — classification, extraction, summarisation, routing — reasoning models are the more expensive and slower choice with no meaningful quality gain. They are a tool, not a default.
The right question
Not: "Should we switch to reasoning models?" But: "What share of our application mix actually requires multi-step reasoning, what is better served by a fast, cheap model, and how do we steer the routing between the two?"