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OpenAI API users face high costs from accumulating context in long runs

By PulseAugur Editorial · [1 sources] ·

Users of OpenAI's Responses API are encountering unexpectedly high costs due to the accumulation of context from previous steps in long-running workflows. While the model's generated output might be short, the input tokens, which include past tool outputs and context, can grow significantly over multiple steps. This leads to variable and often higher token usage for successful runs, prompting users to seek strategies for managing context and reducing costs without sacrificing necessary information. AI

IMPACT Highlights potential cost inefficiencies in complex API workflows, prompting developers to optimize context management.

RANK_REASON User discussion about API cost management for a specific product feature.

Read on r/OpenAI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

OpenAI API users face high costs from accumulating context in long runs

How we ranked this

Signal score
1 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
User discussion about API cost management for a specific product feature.
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.

Full methodology in our editorial standards.

COVERAGE [1]

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

    Long Responses API runs are costing us more from context than the output itself

    <!-- 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…

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