#NaceAI

Small verification models auditing frontier-model or OCR output point to a broader pattern: routing doubts through a cheap checker beats paying for a large model on every token. Teams processing invoices, forms, or scanned archives at volume can adopt the same verify-and-retry loop instead of scaling up model size. Compared with running GPT-6 Astra (low) at US$70.57 per 1,000 pages, the tenfold cost gap makes this relevant to startups and enterprise back offices across ASEAN where document digitisation budgets are tight.

rohanpaul_ai@rohanpaul_aiSource

The implication is that generation is wasted compute when the required output is a choice among known options, and a small classifier-style model can serve that role at a fraction of the cost. Teams building agent routing or tool-selection layers can swap a generative model for Drex in those steps while keeping a larger model for prose tasks. Compared with typical 100B-plus LLMs used for routing, the sub-6B size points to lower latency and cost.

rohanpaul_ai@rohanpaul_aiSource