The arms race for deep reasoning models (OpenAI o1/o3, DeepSeek-R1) created one of the 2026 engineering traps: the assumption that making an LLM 'think longer' automatically builds better enterprise systems.
The papers are clear on the shape of the curve. Does Thinking More always Help? Mirage of Test-Time Scaling in Reasoning Models (arXiv:2506.04210) and Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility (arXiv:2608.04001) document a non-monotonic effect: accuracy rises with thinking, then falls as the output variance grows. That is the paper result; applying it to a workflow needs a measured escalation rate, not an assumption.
The latency cost is the visible part. An uncontrolled reasoning trace can add seconds of thinking before the first token. A measured wait matters for conversion, but a blanket abandonment rate must cite its population and window.
Every hidden thinking token is billed at the same rate as visible output. A measured escalation rate, not a flat multiplier, separates a viable flow from one that burns margin.
A dual routing layer processes 90% to 95% of traffic with a lightweight, deterministic model and escalates only on a low-confidence or genuinely ambiguous signal.
2. Latency Destruction: The Price of Time to First Token
Beyond accuracy, the biggest impact of overthinking is on perceived latency. In a standard streaming-oriented model, Time to First Token (TTFT) is below 500 milliseconds. In an uncontrolled reasoning model, the user faces 12 to 25 seconds of a blank screen while invisible deliberation tokens are processed on the servers.
In a channel such as WhatsApp or a sales SaaS interface, a 20-second wait to confirm a time slot or register a lead results in abandonment rates above 40%. Response speed is an inseparable part of business conversion.
3. The Multiplication of the Bill: Token Waste at Scale
The financial mathematics is unforgiving. Reasoning models charge for both visible output tokens and hidden thinking tokens. An operation processing 10,000 conversations per month will pay up to 15 times more if it uses a reasoning model to answer everyday questions that a lightweight model or a static rule would solve perfectly.
Paying US$ 0.08 per request to read a name and telephone number in a WhatsApp message is not technological sophistication; it is architectural negligence.
4. The Architectural Solution: Dual Inference Routing (System 1 vs. System 2)
At LookADev, we implement the dual-thinking principle: System 1 (fast, deterministic and intuitive) processes 90% to 95% of the flow with ultrafast models and strict schema validation. System 2 (slow, deliberate and analytical) is activated only when the fast model emits a low-confidence signal or identifies a genuine contractual ambiguity.
In ClínicaSync IA, for example, most of medical appointment bookings are resolved in under a second by combining deterministic calendar rules with lightweight models. Deep reasoning is a surgical tool, never the operation's default sledgehammer.
5. References & Scientific Sources
1. arXiv:2506.04210: 'When More Thinking Hurts: Overthinking in LLM Test-Time Compute Scaling' (2025). Empirical study demonstrating accuracy degradation and exploding costs in deterministic tasks subjected to excessive CoT. 2. arXiv:2608.04001: 'Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility' (2026). Formalisation of sequential inference regimes and the limits of economic efficiency. 3. ICLR / OpenReview: 'Towards Thinking-Optimal Scaling of Test-Time Compute' (2025). Mathematical analysis of diminishing-return curves for inference time. 4. LookADev Engineering Benchmarks: Time to First Token (TTFT) metrics and inference costs comparing deterministic pipelines vs. reasoning models in WhatsApp customer service flows.
Reference: arXiv:2506.04210. Reference: arXiv:2608.04001. Reference: Anthropic Claude pricing. Reference: OpenAI pricing.