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Chain of Thought & Reasoning Architecture
A New Model in AI
The reasoning model family (o1 → o3 → OpenAI o3-mini Thinking) represents a fundamental shift. Instead of generating answers token-by-token immediately, they use reinforcement learning to generate a hidden Chain of Thought (CoT) before producing the final output.
The Evolution
| Model | Released | Key Advance |
|---|---|---|
| o1 | Sep 2024 | First reasoning model. No system prompts, no tools. |
| o3-mini | Jan 2025 | Cheaper reasoning with effort levels (low/medium/high). |
| OpenAI o3-mini Thinking | 2026 | Unified reasoning + full API features (tools, system prompts, structured outputs). |
How Reasoning Models Think
- Break the problem into smaller steps.
- Try different approaches.
- Recognize mistakes and backtrack.
- Synthesize a final, accurate answer.
Prompting Reasoning Models
- Keep it simple: State the problem directly. Do NOT say "think step by step."
- Provide edge cases: Give constraints the model should consider.
- Use the developer role: Reasoning models use
developerinstead ofsystem.
// Reasoning models use the "developer" role:
const response = await openai.responses.create({
model: "o3-mini-thinking",
reasoning: { effort: "high" }, // low | medium | high
input: [
{ role: "developer", content: "You are a math olympiad judge. Be rigorous." },
{ role: "user", content: "Prove that sqrt(2) is irrational." }
]
});
⚠️ Anti-Pattern: Adding "think step by step" to a reasoning model prompt actually HURTS performance. The model already reasons internally - forcing a thinking pattern confuses its natural process.
SYNAPSE VERIFICATION
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How do reasoning models differ from GPT-4o?
They are faster
They generate a hidden Chain of Thought before answering, making them more accurate for complex logic
They only generate images
They are cheaper