arXiv cs.MA· Sajid Siraj, Maria Barbati·· 3 天前AI 评分40
小模型何时更绿色？为智能体 AI 合理选型语言模型
When Is Smaller Greener? Right-Sizing Language Models for Agentic AI
AI 导读
一项研究提出按"每成功完成任务"而非每次调用衡量 LLM 的能耗、成本与时间，因为智能体任务需所有步骤都成功，单步可靠性的微小差异会放大为整体成功率的巨大差距。
正文 · 原文
Abstract:AI agents use large language models (LLMs) to plan and act over multiple steps, yet models are usually compared per call rather than per completed task. This paper asks: which model should an agent use to complete tasks reliably while minimising energy, cost and time? We show that per-call comparisons can be misleading for agentic AI. Because a task succeeds only if all required steps succeed, small differences in per-step reliability can produce large differences in overall task success. Failed attempts also consume resources. We therefore evaluate models using energy, cost and time per successfully completed task, treating the completed task as the functional unit. On this basis, we develop a decision model for choosing LLMs in agentic workflows. The model applies to tasks with binary or graded outcomes and to settings with or without retries. We show that each resource has a crossover task length at which a smaller, less reliable model ceases to be the greener, cheaper or faster option. These crossovers divide tasks into three regimes: for short tasks smaller models are preferred, for long tasks larger models are preferred, and only between these extremes do preferences among objectives matter. We embed these results in a two-stage framework that separates efficiency from preference. A Pareto screen first removes models that are never competitive; a weighted decision is required only when a genuine trade-off remains. A pilot study of 15 open-weight models from five developers, ranging from 0.6B to 27B parameters, on two multi-step agent tasks with measured GPU energy supports the predicted structure. The model with the lowest energy per completed task changes with task length and task type, and observed crossover points generally match those predicted from per-step measurements. These findings provide a practical framework for right-sizing language models in agentic AI.
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.19345 [cs.AI] |
| (or arXiv:2610.19345v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19345 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sajid Siraj [view email]
[v1]
Wed, 14 Oct 2026 00:37:23 UTC (88 KB)
来源：arXiv cs.MA · arxiv.org