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Monthly usage limit drains abnormally fast (25% → 5% within ~15 tool calls in one session) #33634
Description
What version of Codex CLI is running?
codex-cli 0.144.5
What subscription do you have?
Plus
Which model were you using?
gpt-5.5 (medium reasoning)
What platform is your computer?
Darwin 24.6.0 arm64 arm
What terminal emulator and version are you using (if applicable)?
ZED
Codex doctor report
{
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"execve wrapper helper": "/Users/szymonnowicki/.codex/tmp/arg0/codex-arg0c5E1fb/codex-execve-wrapper",
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"active rollout files": "2402 files, 153024721 total bytes, 63707 average bytes",
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}
}What issue are you seeing?
Monthly token usage limit drained from 25% remaining to 5% remaining within a single working session, across roughly 15 tool calls (mostly Read/Search on existing files, no large file writes). No corresponding heavy task was run — the calls were standard file reads (Read on ProductController.php, archive-product.blade.php, ArchiveProductController.php, single-product.blade.php, SettingsShop.php, ajax.php, vite.config.js, etc.) in a WordPress/Bedrock/Sage theme repo.
Notably, this session was a resumed session after an earlier prompt was interrupted mid-run:
■ Conversation interrupted - tell the model what to do differently.
I re-sent the same prompt immediately after the interruption. It's possible the interrupted session's context/cache was not properly cleared and got double-counted when the same prompt was re-run, causing inflated token accounting.
For reference, the prior task in the same CLI session reported:
Token usage: total=188 124 input=172 050 (+ 3 523 328 cached) output=16 074 (reasoning 3074)
This is a very large cached-token count relative to the actual output, which raises the question of whether cached context is being over-counted against the monthly limit, especially across resumed/interrupted sessions.
I have never used more than ~50% of a single 5-hour window in normal use, so a monthly limit dropping this fast in one session is inconsistent with my typical usage pattern.
What steps can reproduce the bug?
- Start a Codex CLI session, run a task to completion (e.g. editing CSS/blade files), note token usage report.
- Start a new prompt in the same session.
- Interrupt the conversation mid-run (Conversation interrupted).
- Re-send the same prompt again without starting a fresh session.
- Continue running standard
Read/Searchtool calls (reading existing repo files, no large edits). - Observe monthly usage limit banner dropping rapidly (25% → 10% → 5%) within a small number of read-only tool calls.
Thread/session id: 019f6b9c-a881-7ea1-add9-1da57a2c5d5e
What is the expected behavior?
Monthly usage limit should decrease proportionally to actual token consumption (input + output), and cached/resumed session context should not be double-counted after an interruption. Read-only exploration of a handful of moderately-sized files should not consume ~20% of a monthly quota in a single session.
Additional information
This matches patterns reported in other issues around the same period (e.g. #30918 — "Usage limits draining abnormally fast on Plus: 70% to 100% in about 6 minutes on July 2, 2026"), where OpenAI acknowledged a usage-accounting bug and shipped a fix on June 30, 2026, but users continued to report abnormal drains afterward. Happy to provide sanitized session logs if useful (redacting prompts/file contents).
Activity
github-actions commentedon Jul 16, 2026
Potential duplicates detected. Please review them and close your issue if it is a duplicate.
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pantherstrategy commentedon Jul 16, 2026
Thanks — linking my case to these for visibility. Mechanism looks related (abnormal usage accounting)
but the trigger differs: mine happened on gpt-5.5 medium (not 5.6/xhigh), with no subagents active,
triggered specifically after a
Conversation interrupted+ immediate resume with the same prompt.No thread-list polling storm observed on my end (I'm on Codex CLI, not the desktop app).
Adding 👍 to #32606 and #32591 as likely related root cause (usage-accounting regression),
but keeping this open since the reproduction path is distinct.