o1

Implements a two-phase inference architecture where the model allocates additional compute tokens (up to 32K thinking tokens) to internal reasoning before generating responses. Uses a hidden reasoning layer that performs step-by-step problem decomposition, hypothesis testing, and self-correction without exposing intermediate thoughts to the user. The thinking phase operates on a separate token budget from the response phase, enabling the model to spend variable compute time on problem complexity.

Leverages extended reasoning to achieve expert-level performance on physics, chemistry, and biology problems through multi-step verification and constraint satisfaction. The model internally validates solutions against physical laws, chemical equilibrium principles, and biological mechanisms before responding. Trained on scientific reasoning patterns that enable it to catch errors, consider alternative approaches, and provide rigorous justification.

Generates optimized code solutions for competitive programming problems by reasoning through algorithmic complexity, edge cases, and optimization strategies during the thinking phase. The model evaluates multiple approaches (brute force, dynamic programming, greedy, etc.), analyzes time/space complexity, and selects the optimal strategy before generating code. Handles problems requiring careful input parsing, constraint satisfaction, and numerical stability.

Generates rigorous mathematical proofs by reasoning through logical steps, constraint satisfaction, and symbolic manipulation during the thinking phase. The model constructs proofs incrementally, verifying each step against mathematical axioms and previously established results. Handles problems requiring induction, contradiction, case analysis, and algebraic manipulation with formal rigor.

Provides a 200,000 token context window that accommodates large codebases, long documents, and extensive problem specifications. The context budget is separate from the thinking token budget (up to 32K), allowing the model to maintain awareness of large amounts of reference material while reasoning through complex problems. Enables processing of entire files, documentation, and multi-file code analysis without truncation.

Detects and corrects errors during the reasoning phase by internally testing solutions against constraints, edge cases, and domain principles. The model generates candidate solutions, evaluates them, identifies failures, and iterates without exposing failed attempts to the user. This self-correction loop is performed in the hidden thinking phase, resulting in higher-quality final responses.

Systematically identifies and handles edge cases and constraints during the reasoning phase by enumerating boundary conditions, special cases, and constraint violations. The model reasons through input validation, numerical edge cases (overflow, underflow, division by zero), and domain-specific constraints before generating solutions. This enables robust solutions that handle corner cases correctly.

Allocates compute dynamically based on problem complexity, spending more thinking tokens on harder problems and fewer on simpler ones. The model estimates problem difficulty and adjusts the reasoning phase duration accordingly, resulting in variable latency (5-30 seconds) depending on problem complexity. This adaptive allocation improves efficiency compared to fixed-latency approaches.

Provides access to the o1 model through OpenAI's REST API with support for both streaming and batch processing modes. Developers can integrate o1 into applications via standard HTTP requests, with SDKs available for Python, Node.js, and other languages. Batch processing enables cost-optimized processing of multiple problems asynchronously.