Thinking Costs Tokens: When More Structure is Worth the Price
Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.
Verification-and-planning scaffolding only pays off above roughly 1,500 output-equivalent tokens per call; below that the overhead starves the actual answer, and at 1,000 tokens a plain single LLM call beats verified search 18% to ~0% on financial QA. Even at generous budgets the structured architecture's edge is modest (~44% vs ~40%), so if you're running under tight per-call token caps, drop the agentic scaffolding and just make one direct call—the "thinking" machinery costs more than it returns at low budgets.
Verified search architectures only outperform single LLM calls once you hit **1,500+ output-equivalent tokens**—below that, planning overhead kills accuracy. This means if you’re shipping agents with tight token budgets (e.g., sub-1k), structured reasoning will actively degrade performance; above it, expect a **4% absolute accuracy gain** on complex tasks like financial QA, but only if you can afford the extra tokens.