Verified65% confidenceTradeoffExact time
Large models have high inference costs, large response latency, and steep deployment barriers, making them unsuitable for all scenarios.
3
Sources
65%
Confidence
Long-term
Relevance
9/17/2026
First Seen
Sources
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Related Claims
UnverifiedHaving a large model directly perform thousands of multiply-accumulate operations leads to three outcomes: slow, expensive, and incorrect79% similarUnverifiedThe method significantly increases inference cost, and practical deployment requires weighing latency against quality.78% similarUnverifiedGeneral-purpose large models can cover multiple extraction tasks through prompts, but each call is costly and high-latency.78% similarVerifiedExcessive upfront thinking incurs increased latency and computational costs, which may not be worthwhile in scenarios that prioritize response speed.78% similarUnverifiedHigher-capability models are typically accompanied by higher latency and call costs.78% similar
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