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English(EN) I Read All 471 of My Claude Code Sessions: Where 6 Billion Tokens Actually Went

Claude 代码使用分析揭示了除 token 数量外的成本驱动因素

作者 PulseAugur 编辑部 · [1 个来源] ·

一位用户分析了他对 Claude 代码的大量使用情况，发现缓存读取虽然占了绝大多数 token，但并非主要的成本驱动因素。相反，缓存写入尽管使用的 token 较少，却占了相当大一部分的费用。这项分析强调了仅凭 token 数量可能无法准确了解实际成本，特别是对于像 Claude Opus 5.5 这样具有分级定价结构的模型。 AI

影响 为大型 LLM API 用户（特别是采用分级定价的用户）提供了成本优化方面的见解。

排序理由 用户驱动的对特定产品使用模式和成本结构的分析。

在 dev.to — LLM tag 阅读 →

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Claude 代码使用分析揭示了除 token 数量外的成本驱动因素

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Tool
用户驱动的对特定产品使用模式和成本结构的分析。
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报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Dan Mercede ·

    I Read All 471 of My Claude Code Sessions: Where 6 Billion Tokens Actually Went

    <p>On October 21, 2026, <a href="https://x.com/CampbellKaleb23/status/2112957708219433095" rel="noopener noreferrer">a post on X</a> asking what was going on with Claude Code usage took off. The author said they had never run out of usage like this on any model, and that it was "…

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