openai / openai/codex

Weekly-limit percentage does not correspond to comparable token allocation across two Codex accounts

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Description

Summary

I use two separate Codex accounts, referred to here as Codex and Codex1. They are separate accounts, not different models or modes.

During the comparison workload, both primary accounts used:

  • Model: gpt-5.6-sol
  • Reasoning: max
  • Mode: fast

Both accounts showed 100% weekly limit remaining at the start and 10% remaining when the comparison was made. However, Codex1 received materially less token usage than Codex.

The current Codex1 follow-up chat is using gpt-5.6-luna; that is a later configuration and is marked separately below.

Summary comparison

Account User chats Subagent sessions Models used* Input Cached Uncached Output Thinking Total
Codex 8 14 sol ×8, terra ×9, luna ×5 155,518,847 150,441,600 5,077,247 640,310 319,088 156,159,157
Codex1 5 9 sol ×4, luna ×4, terra ×6 75,250,655 73,071,616 2,179,039 302,457 151,879 75,553,112
Combined 13 23 230,769,502 223,513,216 7,256,286 942,767 470,967 231,712,269

*The model counts include spawned subagents. The primary comparison workload used gpt-5.6-sol with max reasoning on fast mode.

Codex1 received roughly half the total recorded token usage of Codex, despite both accounts moving from 100% to 10% of the weekly limit.

User-chat details

Account Start Session Type Model Input Cached Uncached Output Thinking Total
Codex 20:58 01a02f3c-8a9b user sol 17,786,528 17,489,408 297,120 48,284 24,486 17,834,812
Codex 20:59 01a02f3d-9648 user sol 8,362,927 8,167,424 195,503 35,186 18,461 8,398,113
Codex 20:59 01a02f3d-d42d user sol 12,262,848 11,988,736 274,112 48,703 28,754 12,311,551
Codex 21:21 01a02f52-2804 user sol 7,059,368 6,873,344 186,024 33,444 19,795 7,092,812
Codex 21:26 01a02f56-3b88 user sol 9,530,692 9,325,824 204,868 43,187 22,064 9,573,879
Codex 21:30 01a02f5a-3e8a user sol 10,896,438 10,557,184 339,254 61,980 32,635 10,958,418
Codex 21:42 01a02f65-8ab8 user sol 23,749,331 23,267,712 481,619 77,577 37,578 23,826,908
Codex 21:47 01a02f69-e6f5 user sol 15,120,838 14,744,320 376,518 48,423 25,605 15,169,261
Codex1 20:59 01a02f3d-fda0 user sol 15,823,269 15,561,728 261,541 37,734 19,394 15,861,003
Codex1 21:00 01a02f3e-4c5c user sol 15,021,264 14,727,936 293,328 47,837 27,778 15,069,101
Codex1 21:18 01a02f4f-1da5 user sol 13,019,698 12,776,704 242,994 47,946 30,124 13,067,644
Codex1 21:25 01a02f55-59eb user sol 8,139,868 7,918,848 221,020 37,962 19,406 8,177,830
Codex1 22:29 current follow-up luna 686,190 591,616 94,574 8,611 3,714 694,801

The remaining 23 sessions were spawned scout, test-writer, or implementer agents and are included in the summary totals.

Expected behavior

The weekly-limit percentage should either represent comparable token capacity across accounts, or the UI should expose account-specific quota, weighting, and remaining-token values.

Requested investigation

  1. Why did Codex and Codex1 consume materially different token amounts for the same displayed weekly-limit change?
  2. Does the weekly percentage account for model, account, service-tier, caching, or reasoning-weight differences?
  3. Can exact weekly quota and remaining-token values be exposed?
  4. Is this an expected account-level difference or a weekly-limit display/calculation issue?

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

The report names no files, tests, or entry points. Start by reproducing the two-account comparison and tracing how the weekly-limit percentage is calculated for model, caching, reasoning, and service-tier differences. Done means documenting whether the difference is expected or identifying a display/calculation issue, with account-specific quota information exposed if appropriate.

Written by the indexing model from the issue text.

Assessment

Domain
tooling
Issue type
Bug
Difficulty
5/5
Estimated time
Over a week
Activity status
Active
Clarity
Mostly clear
Newbie friendliness
35/100

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