Reasoning Model
In one sentence
A large language model that internally analyzes a problem step by step before delivering a conclusion, making it ideal for calculations, planning, and complex judgments.
Reasoning models spend more time and tokens "thinking," which makes them slower and more expensive. However, they are far less error-prone on tasks like multi-step calculations, coding, and proposal comparisons.
Standard models are sufficient for simple tasks like rewriting, translating, and summarizing; use reasoning models when tasks require multi-step judgment.
Example
Using AI to compare quotes, lead times, and payment terms from three vendors to recommend a purchasing decision is an ideal task for a reasoning model.
Related terms
- Large Language Model (LLM)
- AI models trained on massive volumes of text that can understand and generate text. Doubao, DeepSeek, ChatGPT, and Claude are all powered by large language models.
- AI Agent
- An AI that can read files, call external tools, and complete multi-step tasks on its own, unlike a standard chat AI that only replies with text.
- Token
- The basic unit LLMs use to process text. Both model context length and API call costs are calculated in tokens.
