Unverified50% confidenceFactExact time
Compared with the default configuration, limiting the maximum number of output tokens can reduce the inference energy consumption of the LLM optimization process by up to 69%.
1
Sources
50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
Sources
Related Entities
Related Claims
Unverified少样本方式在格式化输出、分类任务和规则遵循方面的幻觉率可降低40%-70%73% similarUnverifiedThe optimized version of the model in this solution can reduce thinking time by approximately 63%, with faster output and virtually no loss in quality.72% similarUnverifiedDifficulty-aware routing reduces LLM calls by 85% on the final price task while closing half of the accuracy gap relative to the pure GPT-4o solution.71% similarUnverified领域微调后的7B模型在任务准确率上往往超越未经微调的70B通用模型,同时推理成本降低一个数量级71% similarUnverified混合架构中,仅当规则匹配失败或置信度低于阈值时才调用大模型兜底,通常可减少60%-80%的API调用量70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1098283API
curl https://kongchang.com/api/v1/knowledge/claims/1098283MCP
get_claim(id=1098283)