The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type. Second, Opus 4.7 thinks more at higher effort levels, particularly on later turns in agentic settings. This improves its reliability on hard problems, but it does mean it produces more output tokens.
大多数人认为AI模型升级应该提高效率，减少资源消耗。但作者指出Claude Opus 4.7实际上会产生更多输出token，消耗更多计算资源。这种'效率降低'换取'可靠性提高'的权衡挑战了人们对AI发展必然带来效率提升的认知，表明在某些场景下，模型可能需要更多思考才能达到更好的结果。