Thinking Costs Tokens: When More Structure is Worth the Price
Adding inference structure to a language model lets it search, verify, and revise, but these actions consume the very budget they are supposed to use well. In this paper, we investigate whether there exists a token-budget threshold, below which the overhead of planning and verification hurts performance and above which it helps. We evaluate two systems on FinQA and TAT-QA financial reasoning tasks, using GPT-5.4 mini across 14 budget tiers ranging from 250 to 42,000 output-equivalent tokens. The first system is a monolith, which is a single LLM call. The second is a verified search architecture that adds planning, label-blind checking, and repair capabilities. We run 1,000 cases for a tot...
| Authors | Thomas Nolasque, John Grey, Calista Pham et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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