tl;dr : Interfaze is a new model architecture that outperforms models like Gemini-3-Flash, Claude-Sonnet-4.6, GPT-5.4-Mini, and Grok-4.3 across 9 head-to-head benchmarks in OCR, vision, STT, and structured output. Humans are inefficient at computer-level tasks. We make mistakes, but we're great at decision-making and understanding nuance. Imagine telling a human to read a 50-page PDF, map every word to another document with its XY position, and translate the whole thing into Chinese. You'd get tons of mistakes, pay a lot to keep that human on payroll, and wait a long time for the result. Transformer models are similar. They're amazing at nuance and human-level tasks, and they make mistakes like a human, but that's also what keeps them creative. We've been using the wrong models for the wrong tasks. CNNs/DNNs have existed since the early 90s, from LeNet-5 to ResNet, and more recently CRNN-CTC.…