Expired50% confidenceSolutionExact time
A tiered model invocation strategy that delegates simple intent recognition to small models in the 7B range and routes complex reasoning to GPT-4o can reduce token costs by more than 60%.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/14/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 10/12/2026(expired)
Sources
企业级AI智能体实战：Hermes Agent开发指南
Related Claims
UnverifiedUsing a GPT-3.5-level model for simple tasks costs approximately 1/20 of flagship model pricing, and proper LLM routing can reduce overall inference costs by 60%–80%.82% similarUnverifiedThrough a gradient-based model scheduling strategy, 80% of simple tasks are handled by the cheapest model and only 5% of complex tasks invoke the top-tier model, reducing overall costs by 60–70%.75% similarUnverifiedA strategy of dynamically routing to smaller models can reduce overall costs by more than 60%.74% similarUnverifiedThrough prompt engineering techniques such as streamlining context, optimizing System Prompt design, and using few-shot examples, token consumption for the same quality output can be reduced by 30%–60%73% similarVerifiedA well-designed model routing strategy can reduce overall inference costs by 40% to 70%.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/503356API
curl https://kongchang.com/api/v1/knowledge/claims/503356MCP
get_claim(id=503356)