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LLM benchmarks miss crucial cost calculations, author explains

By PulseAugur Editorial · [1 sources] ·

Benchmarks for LLMs often fail to account for the actual cost of using a model, focusing instead on output quality and token count. The author proposes a simple formula to calculate cost: (input_tokens / 1M) * price_in + (output_tokens / 1M) * price_out. This is crucial because a model that is cheaper per token might end up being more expensive if it requires more tokens to complete a task due to misunderstandings or retries. The article advises logging token usage per task, pricing top model candidates on personal tasks, and recomputing costs quarterly, as model prices and capabilities evolve. AI

IMPACT Highlights the need for cost-aware LLM selection, impacting enterprise adoption and development strategies.

RANK_REASON The item is an opinion piece discussing a methodology for evaluating LLM costs.

Read on dev.to — LLM tag →

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

LLM benchmarks miss crucial cost calculations, author explains

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  1. dev.to — LLM tag TIER_1 English(EN) · Andrey Altrouter ·

    Your model benchmark measures everything except the bill

    <p>Every model benchmark I read ranks the same two things: how good the output was, and sometimes how many tokens it took. Almost none of them print the number you actually get charged for.</p> <p>That gap matters, because the two rankings are not the same list. A model can win o…

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