Spot the errors in this plan to route all traffic through max effort reasoning

There are three claims in the plan, and all three are wrong in different ways. **Error one** is the routing decision. Sending every request, including greetings and FAQs, to the reasoning tier at maximum effort uses the most expensive path for traffic that gains nothing from deliberation. Production systems route by request class: simple lookups to a fast model, complex multi-step questions to the reasoning tier. **Error two** is the claim that more thinking tokens always produce better answers. On easy prompts, more thinking gains nothing once the model has the answer. Worse, overthinking can degrade accuracy: the model second-guesses a correct first instinct or wanders into irrelevant edge cases. Quality versus thinking-tokens is a saturating curve, not a monotonic one. **Error three** is the claim that cost is negligible at scale. Reasoning tokens bill at the output rate, and a max-effort default produces traces of thousands of tokens per call. At even modest scale, hidden thinking dominates the bill. 'Negligible' is the opposite of what max-effort routing produces. A correct plan routes per request, sets effort based on classified difficulty, and measures cost per intent.

**Three distinct errors.** Each one would be a production red flag on its own. **One global route.** Greetings, FAQ lookups, status checks, and password resets all have templated answers. Sending them through max-effort reasoning produces hidden traces that consume thousands of tokens per call with no accuracy improvement. The correct pattern is class-conditional routing: fast model for known templates, reasoning tier only on multi-step queries. **'More thinking is always better' is false.** The thinking tokens versus accuracy curve saturates quickly and can regress. Easy prompts hit a ceiling fast, and excessive deliberation introduces overthinking, unnecessary qualifiers, second-guesses on correct first answers, exploration of edge cases that do not apply. Benchmark studies on 2026 reasoning models show flat or slightly declining accuracy past a point on easy slices. **'Cost is negligible at scale' is the opposite of true.** Thinking tokens bill at the output rate, and max-effort traces routinely run 8,000 to 30,000 tokens per call. At a moderate 100,000 requests per day, that is 100 million to 3 billion output-billed tokens daily. At commodity reasoning-model rates this lands in the hundreds to thousands of dollars per day for a single deployment. Costs scale with traffic, and traffic does not stay small. The correct plan: route per class, set effort by classified difficulty, cap the budget, and measure dollars per intent.