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English(EN) Long Responses API runs are costing us more from context than the output itself

OpenAI API用户在长时间运行时因累积上下文而面临高昂成本

OpenAI Responses API的用户正面临意想不到的高成本,这是由于在长运行工作流中累积了先前步骤的上下文。尽管模型的生成输出可能很短,但输入令牌(包括过去的工具输出和上下文)可能会在多个步骤中显著增长。这会导致成功运行的可变且通常更高的令牌使用量,促使用户寻求管理上下文和降低成本的策略,同时又不牺牲必要的信息。 AI

影响 凸显了复杂API工作流中潜在的成本效率低下问题,促使开发人员优化上下文管理。

排序理由 用户讨论特定产品功能的API成本管理。

在 r/OpenAI 阅读 →

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

OpenAI API用户在长时间运行时因累积上下文而面临高昂成本

本文如何被排名

Signal score
1 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
用户讨论特定产品功能的API成本管理。
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
Same-day
Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

  1. r/OpenAI TIER_2 English(EN) · /u/Immediate_Menu_3695 ·

    长响应API运行的上下文成本高于输出本身

    <!-- SC_OFF --><div class="md"><p>I’ve been looking closer at the token usage from one of our longer-running workflows using the Responses API and a big portion of the tokens on the expensive runs aren’t coming from what the model generates. The first couple steps are pretty norm…