Thinking Costs Tokens: When More Structure is Worth the Price
Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.
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.
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.