Interfaze: A new model architecture built for high accuracy at scale (interfaze.ai)

Interfaze has unveiled a groundbreaking model architecture that surpasses prominent competitors like Gemini-3-Flash and GPT-5.4-Mini across nine benchmarks in various tasks, including OCR and structured outputs. What sets Interfaze apart is its hybrid approach, combining the specialized capabilities of deep neural networks (DNNs) and convolutional neural networks (CNNs) with the flexibility of transformer models, resulting in high accuracy and efficiency for deterministic tasks. This architecture allows for notable task-specific performance, such as exceeding 85% accuracy in complex scenarios like OCR for images and PDFs while maintaining rapid processing speeds. The significance of Interfaze lies in its potential to transform how developers approach deterministic tasks, optimizing cost and speed without sacrificing performance. Unlike traditional models, which can be inflexible and costly to retrain, Interfaze's design enables scalable deployment at a competitive price—$1.50 per million input tokens—while effectively handling multiple input modalities: text, images, and audio. This could revolutionize applications in industries where accuracy and cost-effectiveness are critical, allowing users to perform complex data extraction and processing tasks reliably and swiftly.