Reasoning model
A type of AI model built to work through problems in intermediate steps before answering. Like 'foundation model' or 'agent', it names a category, not an explanation of what a system does.
A reasoning model is a language model designed or trained to spend extra computation working through a problem, breaking it into steps, checking and revising, before it gives a final answer. They tend to do better on multi-step tasks such as maths, code and planning, at the price of more latency and cost.
The label is useful but easy to over-read. "Foundation model", "reasoning model", "agent" and "multimodal AI" name categories. They do not by themselves explain what a system receives, what it stores, what it can retrieve, how it transforms information and where it can act.
The better question
"Can AI reason?" is usually the wrong question. The useful one is which reasoning steps in this workflow are reliable enough, which failures matter, and which failures can be detected before they become consequential. A procurement team cannot buy "reasoning"; it buys a configured system. Evaluate it on your workload with the right benchmark and runtime address, and see LLM and agentic AI.
Read more in The wrong questions about AI right now.
Related terms
LLM
Large language model: an AI model trained on very large amounts of text to predict and generate language, which can be used to answer, summarise, write, reason about and act on text.
Agentic AI
AI systems that pursue a goal by planning, using tools and taking multi-step actions with some autonomy, rather than only answering a single prompt.
Benchmark
A standardised test used to compare AI models. Useful as evidence about someone else's system, but only trustworthy for you when it describes the exact serving route, harness and conditions you will run.