When Is Smaller Greener? Right-Sizing Language Models for Agentic AI

arXiv cs.AIOctober 15, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2610.19345v1 Announce Type: new 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

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