Unverified50% confidenceFactExact time
Chain-of-thought reasoning models may generate hundreds or even thousands of thinking tokens even for extremely simple problems, causing latency to spike and inference costs to surge.
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50%
Confidence
Long-term
Relevance
10/1/2026
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Related Claims
VerifiedThe thinking budget mechanism of reasoning models sets an upper limit on the number of reasoning tokens, and once exhausted, the model cannot output a complete answer, potentially causing overthinking issues.78% similarVerifiedHigh-intensity reasoning configurations typically entail higher costs and longer response times, trading response speed for logical rigor.77% similarUnverifiedFor automation steps with a determined operation path that require no deep reasoning, chain-of-thought introduces significant latency and cost overhead, and reasoning mode should be enabled on demand to reduce costs and improve speed.75% similarUnverifiedCalling models with 70 billion parameters or more often requires multi-GPU clusters, and single-inference latency and cost are unacceptable for high-frequency decision scenarios.75% similar
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