o4-mini

Integrates extended chain-of-thought reasoning directly into the function-calling execution path, allowing the model to reason about tool selection, parameter construction, and result interpretation before and after each function invocation. Unlike models that separate reasoning from tool use, o4-mini interleaves internal reasoning steps with external function calls, enabling the model to adaptively refine tool parameters based on intermediate reasoning outcomes and error feedback.

A distilled reasoning model trained specifically for mathematics, physics, chemistry, and engineering problems, using curriculum learning and domain-specific synthetic data to achieve reasoning quality comparable to larger models at 1/10th the parameter count. The model uses sparse attention patterns and quantized reasoning embeddings to maintain reasoning depth while reducing inference cost and latency, making it suitable for high-volume STEM workloads.

Maintains reasoning context across multiple conversation turns, enabling the model to build on previous reasoning and avoid re-deriving conclusions. The model caches intermediate reasoning results and references them in subsequent turns, reducing redundant computation and improving coherence. This is implemented via a conversation state manager that preserves reasoning tokens and intermediate conclusions across turns, with a mechanism to reference prior reasoning in new responses.

Processes multiple similar problems in a batch, amortizing reasoning costs across the batch by identifying common reasoning patterns and reusing them. The model reasons once about a problem class and applies the reasoning to multiple instances, reducing total reasoning tokens. This is implemented via a batch processor that identifies problem similarity, performs shared reasoning, and applies results to individual instances.

Implements function calling with a built-in feedback loop where the model's reasoning process directly influences parameter construction and tool selection confidence. The model can reason about parameter validity, detect potential errors in tool invocation, and self-correct before execution, reducing downstream errors and failed tool calls. This is achieved through a tightly coupled reasoning-to-function-schema pipeline that exposes intermediate reasoning states to the parameter generation layer.

Generates code across multiple files with reasoning about architectural consistency, dependency management, and refactoring opportunities. The model reasons about code structure before generation, identifying opportunities to extract shared utilities, reduce duplication, and maintain consistent patterns across files. This is implemented via a reasoning phase that builds an abstract syntax tree (AST) representation of the target codebase structure before token generation, enabling structurally-aware code synthesis.

Delivers reasoning model inference with sub-5-second latency for typical problems through optimized token generation and streaming of reasoning tokens in real-time. The model uses speculative decoding and early-exit mechanisms to avoid unnecessary reasoning steps for simpler problems, and streams intermediate reasoning tokens to the client as they are generated, enabling progressive disclosure of reasoning without waiting for completion. This is implemented via a streaming API that exposes reasoning tokens separately from final response tokens.

Automatically adjusts reasoning depth based on problem complexity, using heuristics to detect simple problems that require minimal reasoning and complex problems that need deeper reasoning. The model estimates problem complexity from the input (prompt length, keyword detection, mathematical operators) and allocates reasoning tokens accordingly, reducing costs for simple queries while maintaining quality for complex ones. This is implemented via a complexity classifier that runs before the main model and sets a reasoning budget parameter.

Generates structured outputs (JSON, XML, YAML) that conform to user-provided schemas, with reasoning-based validation ensuring the output matches the schema before returning. The model reasons about schema constraints during generation and self-corrects if it detects a constraint violation, reducing invalid JSON or schema mismatches. This is implemented via a schema-aware token generation layer that constrains the token space to valid schema values and a post-generation validation step that uses reasoning to verify compliance.

Detects and recovers from errors in multi-step agentic workflows by reasoning about failure causes and automatically adjusting strategy. When a tool call fails or produces unexpected results, the model reasons about the error, identifies the root cause, and either retries with adjusted parameters or switches to an alternative approach. This is implemented via an error feedback loop where tool execution errors are fed back to the model with reasoning context, enabling adaptive recovery without explicit retry logic.

Provides code completions that understand the broader codebase architecture, not just local context. The model reasons about the codebase structure, existing patterns, and architectural constraints before generating completions, ensuring suggestions align with the project's design. This is implemented via a codebase indexing layer that provides architectural metadata (class hierarchies, module dependencies, design patterns) to the reasoning process, which then influences token generation for completions.

Solves mathematical problems by reasoning about symbolic representations and algebraic manipulations, not just pattern matching. The model builds symbolic expressions, reasons about mathematical properties, and applies transformations step-by-step, enabling it to solve novel problems not seen in training. This is implemented via a symbolic reasoning layer that represents mathematical expressions as abstract syntax trees and applies reasoning to derive solutions.