OpenAI: o4 Mini High

Implements OpenAI's o-series reasoning architecture with a high reasoning_effort parameter that allocates extended computational budget to internal chain-of-thought processing before generating responses. The model uses a two-stage inference pipeline: first, an internal reasoning phase that explores multiple solution paths and validates logic chains, then a response generation phase that synthesizes conclusions. This approach enables deeper problem decomposition and error correction within the reasoning trace without exposing intermediate steps to the user.

Implements a lightweight variant of the o-series reasoning architecture optimized for reduced parameter count and inference cost while maintaining reasoning capabilities. The model uses knowledge distillation and architectural pruning techniques to compress the full o-series model into a 'mini' form factor that runs faster and cheaper. This enables reasoning-grade problem-solving on a budget suitable for high-volume or resource-constrained applications, trading some reasoning depth for 3-5x cost reduction.

Integrates vision processing capabilities into the reasoning architecture, allowing the model to analyze images, diagrams, charts, and screenshots as part of its reasoning process. The model uses a vision encoder that converts images into a token representation compatible with the reasoning pipeline, enabling the model to reason about visual content, extract information from diagrams, and solve problems that require both visual and logical analysis. This supports use cases like code review from screenshots, diagram interpretation, and visual problem-solving.

Exposes the o4-mini-high model through OpenAI's REST API with support for both streaming and non-streaming response modes. The implementation uses HTTP POST requests to the completions endpoint with configurable parameters (reasoning_effort, temperature, max_tokens) that control inference behavior. Streaming mode returns tokens incrementally via server-sent events, enabling real-time response display; non-streaming mode returns the complete response after reasoning completes. The API handles request queuing, rate limiting, and error recovery transparently.

Supports response_format parameter to constrain model outputs to valid JSON matching a user-provided schema. The implementation uses the reasoning pipeline to generate responses that conform to specified JSON structures, with built-in validation ensuring the output is parseable and schema-compliant. This enables reliable extraction of structured data (e.g., parsed code, categorized analysis, extracted entities) from reasoning processes without post-processing or regex parsing. The schema validation happens during generation, not after, reducing latency and ensuring 100% valid JSON output.

Manages a fixed context window (typically 128K tokens for o4-mini) with built-in token counting to help developers track usage and optimize prompts. The implementation provides a tokens_per_message parameter and token counting utilities that estimate prompt and completion token consumption before making API calls. This enables developers to fit large documents, code repositories, or conversation histories within the context window without trial-and-error. Token counting accounts for special tokens, message formatting, and reasoning overhead.