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English(EN) What an LLM call actually costs in November 2026: open models per 1M tokens

LLM成本分析:输出token比输入token更贵,上下文窗口限制至关重要

一篇博文通过分析Synap API目录的实时定价,详细介绍了使用开源权重大型语言模型(LLM)的实际成本。分析显示,输出token比输入token贵得多,通常是输入token的3-5倍。文章还强调,上下文窗口限制是一个关键因素,无论其每个token的价格如何,都可能使模型无法胜任某些长文档任务。 AI

影响 为开发者和组织提供了关于LLM推理真实成本的实用见解,强调了token使用量和上下文窗口限制。

排序理由 分析LLM定价和性能的博文,并非主要发布或重要的行业事件。

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LLM成本分析:输出token比输入token更贵,上下文窗口限制至关重要

本文如何被排名

Signal score
9 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Commentary
分析LLM定价和性能的博文,并非主要发布或重要的行业事件。
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
infra, product
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。

报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Synap by Linkrra ·

    2026年11月LLM调用实际成本:每100万token的开源模型

    <p>"$0.017 per million tokens" is a price per token. Your bill is per request, and the two can rank models differently. This post pulls live prices for six open-weight models and prices one realistic workload in realtime and in batch. Then it sends the same request to each model …