Unverified50% confidenceBenchmarkExact time
斯坦福大学开源的RouteLLM系统实测显示，在保持GPT-4级别输出质量的前提下可将成本降低40%-70%
1
Sources
50%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
Frugon：本地分析LLM调用，精准识别可降本请求
hackernewshackernews7/7/2026
Related Claims
UnverifiedFrom GPT-3.5 to GPT-4o mini, the token cost for handling the same tasks has dropped by more than 90%.81% similarUnverifiedFrugalGPT is a study published by Stanford in 2023 that demonstrated that through cascade strategies, inference costs can be reduced by up to 98% while maintaining GPT-4-level quality.78% similarUnverified斯坦福大学2024年发布的RouteLLM框架研究了三类路由方法，发现在MT-Bench等基准上合理路由策略可在保持95%质量下将GPT-4调用量压缩至约30%，成本节省高达70%77% similarUnverifiedFrugalGPT（Chen et al., 2023）提出基于级联调用的路由框架，先用最廉价模型回答，仅当置信度低于阈值时升级到更昂贵模型，实验表明可在保持 GPT-4 级别准确率的同时削减约 98% 的 API 费用76% similarUnverifiedSome wrapper services use lower-spec models to reduce costs, such as passing off GPT-3.5 as GPT-4.76% similar
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