OpenAI o2 sits in the reasoning model family between o1 and o3, targeting a balance of reasoning depth and inference efficiency. It is designed for tasks that require structured thinking and deliberation but where the full compute budget of o3 would be excessive or too slow for the use case.
The model applies chain-of-thought reasoning internally, making it stronger than standard text models on logic puzzles, mathematical derivations, and multi-step coding problems. For applications that need better-than-o1 reasoning without the cost and latency of o3, o2 occupies a practical middle ground.
Key Features
Internal chain-of-thought reasoning for structured problem solving
Stronger logical inference than standard GPT-4-class models
Efficient reasoning trade-off positioned between o1 and o3
Well-suited for mathematics, coding, and formal logic tasks
Reduced hallucination on verifiable, step-dependent problems
Compatible with OpenAI reasoning model API patterns
Ideal Use Cases
Complex coding challenges and algorithm design
Mathematical problem solving and step-by-step derivations
Multi-step planning tasks in business or engineering
Structured legal or policy analysis
Educational platforms requiring rigorous, shown reasoning
Example Prompts for o2
Technical Specifications
| Provider | OpenAI |
| Category | Reasoning |
| Modality | Text -> Text (reasoning) |
Frequently Asked Questions
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