hard20 pts · first answer countstransformersreasoninginference

Reasoning model for simple extraction

A team moves an invoice field-extraction task (GSTIN, date, total) from a fast model to a large reasoning model. Accuracy is unchanged, but cost and latency rise a lot. What is the best lesson?

A.Reasoning models are always more accurate, so the test data must be wrongB.Raise the reasoning budget until accuracy goes upC.Disable structured output so the reasoning model can think freelyD.Reasoning tokens help multi-step problems, not simple lookups; use the cheaper model

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Reasoning model for simple extraction

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