o3

Implements a variable-depth reasoning engine that allocates computational budget across problem-solving steps, allowing users to trade inference cost for solution quality through explicit compute parameters. The model internally expands reasoning chains dynamically, spending more tokens on harder subproblems while maintaining efficiency on simpler steps. This architecture enables breakthrough performance on tasks requiring 10+ logical steps without proportional cost increases for straightforward problems.

Generates code solutions by internally decomposing problems into logical subcomponents and reasoning through implementation strategies before synthesis. The model applies extended reasoning to understand algorithm correctness, edge cases, and optimization tradeoffs before producing code, resulting in fewer bugs and better algorithmic choices. Supports generation across multiple programming languages with language-specific reasoning about idioms and performance characteristics.

Designs system architectures by reasoning about scalability, reliability, and operational constraints. The model can propose component structures, data flow patterns, and deployment topologies while reasoning about trade-offs between consistency, availability, and partition tolerance. Uses extended reasoning to validate architectural decisions against non-functional requirements.

Generates formal and informal mathematical proofs by reasoning through logical steps, constraint satisfaction, and proof strategies. The model internally explores proof paths, backtracks on dead ends, and applies domain-specific reasoning about mathematical structures before committing to a proof outline. Supports competitive mathematics problems, theorem proving, and rigorous derivations with explicit step-by-step reasoning chains.

Reasons through complex scientific problems requiring domain knowledge integration, hypothesis formation, and multi-step experimental or theoretical analysis. The model applies extended reasoning to synthesize information across scientific domains, evaluate competing explanations, and construct rigorous arguments about scientific phenomena. Supports physics, chemistry, biology, and interdisciplinary problems with reasoning that mirrors expert scientific thinking.

Solves abstract reasoning and pattern recognition problems from the ARC-AGI benchmark through extended reasoning about visual patterns, logical rules, and transformation operations. The model reasons about grid transformations, object relationships, and implicit rules by exploring hypotheses about pattern structure before predicting outputs. Achieves 87.5% accuracy on ARC-AGI through reasoning that mimics human visual-logical problem-solving.

Decomposes complex multi-step tasks into logical subtasks and reasons through execution strategies, dependencies, and resource allocation. The model internally explores task decomposition alternatives, identifies critical path items, and reasons about optimal execution order before providing a plan. Supports tasks spanning code generation, research, analysis, and problem-solving with explicit reasoning about task structure.

Solves complex problems by reasoning through edge cases, boundary conditions, and exceptional scenarios before providing solutions. The model internally explores potential failure modes, validates assumptions, and reasons about robustness before committing to answers. Applies to code generation, mathematical problems, and logical reasoning where edge cases significantly impact correctness.

Exposes o3 reasoning capabilities through OpenAI's REST API with parameters allowing users to specify reasoning intensity (low/medium/high or equivalent cost-quality knobs). The API abstracts internal reasoning allocation, handling variable-depth computation transparently while providing consistent response formats. Supports both synchronous and asynchronous inference patterns with streaming or polling for long-running reasoning tasks.

Reasons about problem structure and context to allocate reasoning resources effectively, spending more computation on genuinely difficult subproblems while maintaining efficiency on straightforward parts. The model internally analyzes problem complexity, identifies critical reasoning points, and adapts reasoning depth accordingly. This enables efficient reasoning that scales with problem difficulty rather than fixed token budgets.

Generates API specifications, schemas, and interface designs by reasoning about use cases, consistency, and extensibility. The model can design RESTful APIs, GraphQL schemas, or gRPC services with consideration for versioning, backward compatibility, and performance. Uses extended reasoning to explore design alternatives and validate consistency across endpoints.