Reasoning Model

Reasoning models, pioneered by OpenAI's o1 series and followed by models like Claude's extended thinking, DeepSeek R1, and Gemini's reasoning variants, represent a distinct paradigm from standard LLMs. While a standard LLM generates a response directly from the prompt, a reasoning model first produces an extended internal "thinking" process — working through the problem step by step, exploring multiple approaches, identifying and correcting errors in preliminary reasoning, and only then committing to a final answer. This thinking process may use thousands or tens of thousands of additional tokens that the user doesn't see directly but that fundamentally shape the quality of the final output. The result is dramatically better performance on tasks that require genuine multi-step reasoning: mathematical proofs, complex coding problems, nuanced logical analysis, and situations where the right answer depends on carefully working through multiple interacting constraints.

The performance gains from reasoning models on difficult tasks are substantial but come with tradeoffs. A reasoning model call takes significantly longer — sometimes 30-90 seconds for complex problems — and costs more tokens because the thinking process itself consumes significant compute. For simple questions where a direct answer is readily available, reasoning models provide no benefit and waste time and money. The practical heuristic is task complexity: if the problem requires multiple logical steps, involves competing considerations that need to be weighed carefully, or has benefited from chain-of-thought prompting in standard models, a reasoning model will likely produce meaningfully better results. If the problem is straightforward information retrieval or a simple question, a standard model with lower latency is the right choice.

For B2B teams, reasoning models change the calculus for AI-assisted analysis and decision support. A reasoning model analyzing a complex competitive landscape, evaluating whether a contract clause creates legal risk, or debugging a subtle architectural issue in a codebase can produce analysis comparable to a thoughtful human expert — taking longer but reaching conclusions that standard LLMs would miss or state incorrectly. The product design implication is matching model to task: not every interaction benefits from extended reasoning, and routing simple tasks to fast standard models while directing genuinely complex analytical tasks to reasoning models is both a cost optimization and a quality improvement. As reasoning capabilities continue to improve and costs decrease, this class of model will handle an increasing share of knowledge-intensive professional workflows.