Unverified50% confidenceFactExact time
Cascading is a sequential execution strategy in which a request first goes to a low-cost model, and if its confidence or a verifier determines the result is unreliable, the same request is escalated to a stronger model for a retry.
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10/12/2026
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VerifiedThe Cascading model strategy first has a small model handle requests, and when confidence falls below a threshold, it automatically escalates to a large model for reprocessing, in order to maximize cost efficiency.79% similarUnverifiedSpeculative sampling uses a cheap draft model to generate multiple candidate states in parallel, which are then verified in parallel by the target model, reducing the number of target model calls while keeping the target distribution unchanged.76% similarUnverifiedA tiered evaluation strategy refers to using small models or rules for initial screening and only employing expensive large model judges at critical junctures75% similarUnverifiedSpeculative execution refers to sending requests to both large and small models simultaneously, accepting the small model's result if quality meets the standard and discarding the large model's result; however, this introduces additional computational resource waste and is only worth considering in latency-extremely-sensitive scenarios.74% similarUnverifiedModel selection should involve running the same set of representative use cases through different candidate models, comparing classification accuracy, recall on sensitive cases, latency, and cost, with decisions made by evaluation rather than by model labels73% similar
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