# Interfaze Interfaze (interfaze.ai, currently in beta) is building a model focused on deterministic structured-output tasks — parsing invoices, medical records, transcripts, OCR, PDF-to-database. Their pitch is that general-purpose frontier models do this work badly enough that a specialized model trained against the right metrics is worth a separate product. In April 2026 they published the [Structured Output Benchmark (SOB)](https://github.com/JigsawStack/sob) — see [[structured-output-benchmark]] — with [arXiv 2604.25359](https://arxiv.org/abs/2604.25359), a [HuggingFace dataset](https://huggingface.co/datasets/interfaze-ai/sob), and a [hosted leaderboard](https://interfaze.ai/leaderboards/structured-output-benchmark). The benchmark places their own Interfaze-Beta model at #6 (Overall 0.855) on the unified leaderboard, behind GPT-5.4, GLM-4.7, Qwen3.5-35B, Gemini-2.5-Flash, Qwen3-235B, and ahead of Claude-Sonnet-4.6, GPT-5, Gemma-3-27B, and others. ## What's interesting beyond the model The benchmark is the more durable contribution. By splitting the score into seven metrics across three modalities (text/image/audio) and weighting by schema complexity, SOB exposes the [[json-pass-value-accuracy-gap]] — JSON Pass Rate clears 95%+ on every frontier model, but exact leaf-value accuracy sits 15-30 points lower. That gap is the single most useful corrective to "structured output is solved" claims circulating in 2026. Interfaze's framing of their own positioning is honest about the conflict: they're publishing a benchmark on which their product will be measured. The implicit bet is that being credibly measured is worth more than gaming a private internal score. ## Coverage in the wiki - [[structured-output-benchmark]] — full coverage of SOB, the methodology, and the headline numbers - [[json-pass-value-accuracy-gap]] — the standalone concept that's the benchmark's load-bearing finding URL: