A long Codex session can feel slower or consume more of your plan because the work may involve a growing conversation history and repeated tool-and-model cycles. Those are plausible mechanisms, not proof of what caused any particular lost hours: task complexity and temporary service-side latency can also matter.
Why a long conversation can feel heavier
Codex works in a loop: the model can request a tool, the Codex harness runs it, and the result is added to the prompt for another model inference. A turn can include many of these iterations. When you send another message in an existing conversation, its history is carried forward; file contents, command output, searches, and earlier exchanges can therefore make the prompt larger than it was at the start.
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OpenAI describes this effect in its Codex engineering article, “Unrolling the Codex agent loop”: “This means that as the conversation grows, so does the length of the prompt used to sample the model.” A larger carried prompt is a reasonable explanation for why an extended task may feel less responsive, but the documentation does not quantify a time penalty or establish that context caused a particular slowdown.
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A model’s context window is a token limit for a single inference call, not a timer for how long a Codex session has been open. OpenAI’s conversation-state documentation explains that context can include input and output tokens and, for some models, reasoning tokens. The amount of context used can change as the work and conversation change.
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What compaction does
When context needs to be managed, compaction reduces its size while preserving state needed to continue. OpenAI’s compaction guide treats this as a balance involving quality, cost, and latency. It is not a cost-free reset, nor does the documentation say it guarantees memory loss. The guide’s implementation details are for developers using the Responses API; they do not mean every Codex client exposes those parameters to its users.
More usage does not necessarily mean more waiting
Elapsed time and plan usage are related questions, but not the same measurement. OpenAI’s Codex usage guidance says consumption varies with the model, where the task runs, complexity, context, reasoning, speed, and tools. A long-running task can use substantially more than a short request, but there is no universal rate or predictable cost per hour in that guidance. Check the usage display in your account for the status that applies to your plan rather than relying on a fixed estimate.
More tool activity can mean more work as well as more material carried into later model calls. A short question and a task that repeatedly reads files, runs commands, and processes large outputs are not equivalent simply because they take place in the same conversation. The public guidance identifies these factors; it does not provide a formula that lets you infer usage or response time from session duration alone.
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A gradual change during work that accumulates file reads and tool output is consistent with growing context or a demanding task. A sudden slowdown is a reason to check for service-side problems before blaming the conversation. These patterns help frame a diagnosis; neither proves the cause on its own.
There is precedent for a temporary service issue: OpenAI Status recorded a Codex context-compaction latency incident on May 27–28, 2026, attributed it to a configuration error, and marked affected services recovered. That incident shows that latency can be service-side, but it is not evidence that any other session coincided with an outage. If a delay starts abruptly, note when it began and consult the OpenAI Status history.
What to observe in your own Codex work
If you want to understand what changed, keep observations specific rather than treating session length as the explanation. Compare only work you actually observed, and record enough context to distinguish a heavier task from a service hiccup.
- Note the approximate date, client, model, and task, along with whether the change was gradual or sudden.
- Track how many files, searches, command runs, or large logs were involved, and whether outputs were fed back into the conversation.
- Record whether compaction appeared and what the account usage display showed; neither observation alone identifies the cause of latency.
- For a comparison with a fresh conversation, note what task and inputs changed. Without controlling those factors, it is an observation, not a controlled test.
A public Codex GitHub issue includes detailed telemetry from one tool-heavy run. That is an individual report, not a representative benchmark, and its suggested causes are hypotheses. It can illustrate the kind of activity an advanced user might inspect, but it cannot establish a typical slowdown, compaction frequency, or number of hours lost.
Ways to keep future work focused
For a future task, try giving Codex a focused request and keeping a concise note of project decisions and current state that you can reuse. This can help you avoid carrying irrelevant conversation material into the work. Treat it as a workflow experiment: public documentation explains context growth and compaction, but does not promise that restarting a conversation will improve speed or reduce account usage.
The available public sources do not establish a typical long-session slowdown, a standard number of hours lost, or the cause of the experience behind this title. A larger carried prompt, tool and task activity, and temporary service latency are distinct possibilities; the evidence needed to distinguish them is specific to the session.
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