Understanding the GPT-o1 Reasoning Model
GPT-o1 is a family of "reasoning models" trained to spend more compute on thinking through problems before answering, especially on math, code, and scientific tasks. Instead of just predicting the next token, o1 generates a long internal chain of thought, iteratively refining its approach and correcting mistakes before emitting a final answer.
GPT-o1 vs GPT-4o: Quick Comparison
GPT-o1 Strengths:
- Deep reasoning
- Multi-step problems
- Math & code
- Slower (trade-off)
GPT-4o Strengths:
- Fast responses
- Conversational
- General tasks
- Lower latency
Core Prompting Principles for o1
Advanced prompting for o1 is less about forcing chain of thought and more about sharply defining goals, constraints, and verification.
Keep prompts simple and direct
Short, focused questions with minimal extraneous context let o1 allocate its "thinking" to the core problem.
Skip explicit chain-of-thought prompts
o1 already performs internal reasoning. Prompts like "think step by step" are usually unnecessary.
Use clear delimiters and structure
For complex inputs, mark sections: INPUT, CONSTRAINTS, GOAL so o1 parses reliably.
Specify constraints and success criteria
Reasoning models respond well to explicit boundaries (time/space limits, allowed algorithms) and crisp definitions of correct solutions.