Interfaze Beta utilizes a unique Mixture-of-Experts architecture that prioritizes task-specific efficiency over monolithic processing. At its core, a custom-trained router evaluates user input to determine the necessary difficulty and task type, directing queries to a suite of small language models optimized for specific functions like OCR, classification, and object detection. This design is supported by a robust context-construction layer that parses external data—including web pages, code, and PDFs—into structured states, allowing the system to perform complex operations like headless browsing and secure code execution with high precision.

The system employs a selective escalation strategy, where a thin controller manages the workflow and only engages a stronger generalist model when necessary. By distilling context before it reaches the final generation stage, the model achieves high performance on rigorous benchmarks such as MMLU-Pro and LiveCodeBench, while maintaining reliability in structured output. This approach ensures that the model remains grounded in its actions, making it a highly effective tool for developers who require consistent, accurate results across web scraping, data extraction, and automated coding workflows.