English(EN) The cheapest model per million tokens was the most expensive per request

LLM每次请求成本差异大，输出token是关键

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-24 08:13

最近对LLM定价的分析显示，每百万token定价最低的模型并非总是每次请求成本效益最高的。研究发现，输出token显著驱动了每次请求的成本，通常占总费用的90%以上。延迟也与输出长度相关，这意味着生成更多token的模型往往速度较慢。作者建议，在做出明智决策时，应衡量不同任务形状下的实际使用情况，而不是依赖每百万token的定价。 AI

影响强调输出token生成是LLM API调用的主要成本驱动因素，影响费用和延迟。

排序理由文章分析了LLM的定价和使用情况，提供了见解，而非宣布新产品或研究。

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Signal score: 10 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Commentary; 文章分析了LLM的定价和使用情况，提供了见解，而非宣布新产品或研究。
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: product, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

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dev.to — LLM tag TIER_1 English(EN) · chenyu · 2026-10-24 08:13

The cheapest model per million tokens was the most expensive per request

I measured nine model-task combinations on the same endpoint. Per-million pricing predicted almost none of it. Every "which model is cheapest" post compares price per million tokens. That number is easy to find and almost useless for deciding what to run. <…

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The cheapest model per million tokens was the most expensive per request

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