Reasoning Model, explained
A reasoning model is an AI model specifically trained to think through problems in extended internal steps before producing a final answer, making it significantly more accurate on complex analytical, math, or multi-step tasks.
Standard language models produce an answer in a single forward pass — essentially going straight from your question to a response. Reasoning models are trained differently: they spend time generating a chain of internal reasoning (sometimes called a 'thinking trace' or 'scratchpad') before committing to a final answer. This process can take seconds to minutes for hard problems, and it draws substantially more compute.
The payoff is real. On benchmarks involving math, logic, law, and science, reasoning models significantly outperform their non-reasoning counterparts of similar size. They catch errors they would have made if they had answered immediately. OpenAI's o-series, Anthropic's Claude with extended thinking, and Google's Gemini thinking models all fall into this category.
The tradeoff is cost and speed. Reasoning models are slower and more expensive per query than standard models. For straightforward tasks — summarizing text, drafting emails, basic Q&A — a standard model is usually the better choice. Reasoning models earn their overhead on problems that genuinely require multi-step logic and where getting it wrong is costly.
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Wield's AI Foundations track covers this hands-on, in plain English, with real examples and a copy-paste prompt to try it yourself.
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