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New metric proposed to measure AI agent efficiency beyond raw token counts

A user on Reddit has proposed a new metric called the Agentic Efficiency Ratio (AER) to better measure the performance of AI agents and workflows. This ratio accounts for the varying costs and densities of different token types, such as output tokens, fresh input tokens, and cached read tokens. The goal is to assess how much valuable output an AI system generates per unit of fresh context consumed, with a AER above 1% considered very good for large-scale operations. AI

IMPACT This proposed metric could help developers optimize AI agent workflows by focusing on output density rather than just token volume.

RANK_REASON User-generated proposal for a new metric, not a release or official announcement.

Read on r/OpenAI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New metric proposed to measure AI agent efficiency beyond raw token counts

COVERAGE [1]

  1. r/OpenAI TIER_2 English(EN) · /u/SpellSlinger69 ·

    I stopped measuring AI by raw tokens and built a ratio to see if my setup is actually efficient (AER)

    <!-- SC_OFF --><div class="md"><p>If you run agents or heavy AI workflows, raw token counts are misleading. They blur everything: an output token is dense and expensive, a fresh input token is standard, and a cached-read token costs ~10% of a normal one.</p> <p>So I use a simple …