Following the release of OpenAI version 5, one question keeps surfacing: When should you use the fast model and when should you use the thinking model? The answer is simpler than it appears. It is not about which one is better, but about matching the thinking mode to the nature of the task.
This principle is not exclusive to OpenAI. Every leading LLM provider, including Claude and Gemini, offers models built for speed alongside models designed for deeper and more deliberate reasoning. The same guidelines apply regardless of the platform.
In simple terms, the fast model works by producing the first good answer it can find based on patterns it already knows, while the thinking model takes extra steps behind the scenes – checking, comparing, and refining before it gives you the result.
The Fast Model is the right choice when momentum is the priority. It provides immediate responses, making it ideal for drafting an initial version, retrieving quick facts, or conducting a first-level sort. It helps keep work in motion without investing unnecessary depth in the early stages.
The Thinking Model is essential when accuracy, nuance and accountability matter. It is best used when the task has high sensitivity, broad impact, or when you will need to justify the decision later. It tests assumptions, adds context and identifies subtle factors that can make a significant difference in the outcome.
How to decide? The R N T test:
- Risk – What happens if the answer is wrong? If the cost is high → Thinking
- Nuance – Does the task require sensitivity to tone or deep context? → Thinking
- Traceability – Will you need to explain the decision in the future? → Thinking
In practice:
- Routine low risk tasks → Fast
- Medium importance decisions → Start Fast, validate with Thinking
- Critical or irreversible decisions → Thinking from the start
The conclusion: The real question is not which model is better, but which thinking mode the task demands. Selecting the right approach means moving quickly when speed is all that matters, and thinking deeply when precision and context cannot be compromised – whether you are using OpenAI, Claude, Gemini or any other LLM.
Sometimes the smartest choice is not about pushing harder or slowing down, but about knowing which gear to be in. The better we match the mode to the moment, the more naturally great work gets done.
