Interfaze: A new model architecture built for high accuracy at scale

Interfaze: A new model architecture built for high accuracy at scale copy markdown

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.

These are deep neural network architectures that are task-specific for things like OCR, translation, or GUI detection. The way they consume and see data is trained to be task specific, which makes them up to 100x more accurate at their specific task. They also produce useful metadata like bounding boxes and confidence scores, letting developers build predictable workflows they can rely on.

So why do so many of us still go for transformers/LLMs for deterministic tasks?

DNNs are not flexible. They're only as good as their training data, and they aren't great at human-level nuance.

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