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English(EN) I compiled LLM inference pricing across 7 providers — the caching numbers are surprising(spreadsheet included) [R]

LLM推理定价在7家供应商之间进行比较，突出缓存成本

作者 PulseAugur 编辑部 · [1 个来源] ·

一位用户编制了一个电子表格，比较了包括OpenAI、Anthropic、Cohere和Mistral AI在内的七家供应商的LLM推理定价。比较侧重于输入/输出令牌定价、上下文窗口和缓存输入成本，而不是性能基准。一个关键发现是缓存输入定价的显著差异，其成本可能比非缓存输入便宜数十倍，这使得它成为代理和RAG管道等应用程序的关键因素。 AI

影响 强调了缓存成本对LLM推理的重要性，可能影响应用程序设计和供应商选择。

排序理由 用户生成的LLM推理定价和功能比较。[lever_c_demoted from research: ic=1 ai=0.7]

在 r/MachineLearning 阅读 →

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

LLM推理定价在7家供应商之间进行比较，突出缓存成本

本文如何被排名

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
用户生成的LLM推理定价和功能比较。[lever_c_demoted from research: ic=1 ai=0.7]
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
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
102 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Technomadlyf ·

    我整理了7家提供商的LLM推理定价——缓存数据令人惊讶（附电子表格）[R]

    <table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1ueavxn/i_compiled_llm_inference_pricing_across_7/"> <img alt="I compiled LLM inference pricing across 7 providers — the caching numbers are surprising(spreadsheet included) [R]" src="https://preview.redd…

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