How Reasoning Models Differ
Internal chain-of-thought vs standard models: what changes for the prompt author.
What Are Reasoning Models?
Reasoning models are LLMs specifically trained and configured to run extended internal deliberation before producing a response. Examples include OpenAI's o1/o3/o4 series and Anthropic's Claude with extended thinking enabled.
Unlike standard models that generate text token-by-token directly from your prompt, reasoning models first produce a lengthy internal chain-of-thought, then summarize it into a final answer.
Standard vs Reasoning: What You See
From the API user's perspective, the difference is:
- Standard model: Input → Output (fast, direct)
- Reasoning model: Input → [Internal thinking, hidden or streamed] → Output (slower, more accurate on hard problems)
The thinking process is the model's private scratchpad. It may contain wrong turns, self-corrections, and intermediate calculations that never appear in the final answer.
All lessons in this course
- How Reasoning Models Differ
- Effective Prompts for Extended Thinking
- When to Use Reasoning vs Standard Models
- Cost and Latency Tradeoffs