Reasoning model
A language model trained to work through intermediate steps before producing an answer, typically spending more computation at inference time on harder problems. Reasoning models tend to perform better on multi-step analysis, planning and coding tasks.
Why it matters
Reasoning models can make agents and analytical assistants more capable, but they are usually slower and more expensive per request, and their extra steps do not guarantee a correct or well-grounded answer.
Choosing a model should follow evaluation on the organisation’s own tasks. Many production workflows combine a reasoning model for difficult steps with faster, cheaper models for routine classification or extraction.
In practice
For example, an energy trading analytics team might use a reasoning model to draft a structured explanation of an unusual price movement from several data sources, while a smaller model handles routine tagging of market news.
Where Rodan fits
Rodan selects and combines models based on measured task performance, cost and latency in Applied AI Engineering work. See also how to measure AI performance beyond accuracy metrics.