AI换模型干活，交接要收税
让AI写代码时中途换更强的模型，你以为能救场，结果只追回不到一半的质量差距，还多花一大笔钱。研究者把这种损失叫「交接税」:新模型接手的是别人干了一半的活，它得顺着别人的思路走，反而发挥不出自己的本事。有意思的是，反过来从强模型换到便宜模型，交接反而划算——省了钱，质量掉得不多。而且交接时该给新模型看多少旧记录，方向不同结论正好相反：换强模型时，旧记录越少越好；换便宜模型时，旧记录越多越好。这不是你明天能直接用的技巧，但它解释了为什么「不行就换更强的AI」这个直觉，在长任务里常常不灵。
📄 原文摘要(英文)
Coding agents perform long-running tasks spanning dozens of model calls, tool uses, and code edits. As these runs unfold, users face a practical cost-quality trade-off: escalating to a stronger model when a cheaper one struggles, or downshifting once the hard reasoning is complete. Each switch requires the receiver to continue a non-native trajectory produced by another model. We study how this handoff affects quality and cost, and how varying the trajectory information inherited by the receiver changes the outcome. Using pairs of low-cost, low-capability (LC) and high-cost, high-capability (HC) models from the Claude and GPT families, we vary handoff direction, timing, and interface, comparing full-trajectory transfer, compaction, and trajectory removal while preserving the repository state. Across both model families, full-trajectory escalation recovers less than half of the LC-to-HC quality gap while incurring a substantial cost premium. We term this cost-quality penalty the handoff tax. By contrast, downshift offers a favorable cost-quality point. Interestingly, the preferred interface also reverses with direction: reducing LC-model trajectory information improves escalation quality, whereas removing the HC-model trajectory reduces downshift quality.