IMPORTANT drain usage on /review task [x2 times]
What version of Codex CLI is running?
codex-cli 0.114.0
What subscription do you have?
Pro
Which model were you using?
gpt-5.4 high
What platform is your computer?
Linux 6.8.0-101-generic x86_64 x86_64
What terminal emulator and version are you using (if applicable)?
Tabby
What issue are you seeing?
I tried using /review (again), a new feature offered by OpenAI, and whilst using it, my weekly limits dropped to 0%, whilst my code review usage is still at 100%... I can’t explain how this is possible, as I haven’t used the following:
- I don’t use fast mode
- I don’t use the context window expansion to 1M tokens (I’m using the default 256k)
- I haven’t used multi-agents
- I haven’t used the xhigh version, only medium and high in the last few days
- I only use this in config.toml: '[mcp_servers.context7]
args = [‘-y’, ‘@upstash/context7-mcp’, ‘--api-key’, ‘ctx7sk-a5851794-f6d1-4435-b17f-fa9306021567’]
command = ‘npx’
enabled = true
[mcp_servers.playwright]
command = ‘npx’
args = [‘-y’, ‘@playwright/mcp@latest’, ‘--headless’, ‘--no-sandbox’, ‘--isolated’]
enabled = false # disables the server
[notice]
hide_gpt5_1_migration_prompt = true
‘hide_gpt-5.1-codex-max_migration_prompt’ = true
hide_rate_limit_model_nudge = true
[notice.model_migrations]
‘gpt-5.1-codex-max’ = ‘gpt-5.2-codex’
approval_policy = ‘on-request’
sandbox_mode = ‘workspace-write’
‘gpt-5.1-codex-mini’ = ‘gpt-5.3-codex’
[sandbox_workspace_write]
network_access = true
web_search = ‘live’
[features]
unified_exec = true
shell_snapshot = true
steer = true
personality = true
prevent_idle_sleep = true
[[skills.config]]
path = ‘~/.codex/skills’
enabled = true'
<img width="1229" height="410" alt="Image" src="https://github.com/user-attachments/assets/bc894340-40cc-4064-bbc8-7d7eed865bf6" />
What steps can reproduce the bug?
Uploaded thread: 019cebca-6ed2-78d3-aece-11548c592648
You can use /review – I reported this issue a few days ago; I thought it had been resolved, but it hasn’t been yet
What is the expected behavior?
I would simply like to see only the appropriate charges applied, as that is what I expect from a $200 subscription.
Additional information
Here is my user ID so you can check more easily: user-grEQqq7dokHb56wxlYbBeUWO
Tell me if im doing something wrong.
Showing cached comments. Read the full discussion on GitHub ↗
11 Comments
Potential duplicates detected. Please review them and close your issue if it is a duplicate.
Powered by Codex Action
Codex review usage is for in GitHub with "@codex review", using /review in CLI comes out of normal usage. It would be nice if we could use /review in CLI and it came out of this usage, doing it in GitHub does not suit my workflow.
Sidenote, 5.4 does use a lot of usage for /review in my experience but it is quite thorough. Better to maybe use a different model for /review if you don't need it to be so thorough.
As @DeanStr said, code review usage is for the cloud-based
@codex review. Local/reviewcomes out of your normal local usage budget. So the behavior you're seeing here is expected, not a bug.@YoloMark98 not sure if you're aware you exposed your context7 api key, you should rotate it.
Yes, thanks.
Yes, but I still can’t figure out how I managed to use up almost the entire context window of my Pro subscription in a single day, isn’t that strange? Especially as I don’t use fast mode and haven’t made any changes to the context window tokens.
@YoloMark98, it depends on the nature of the source base and the code review. Some code reviews require significant work on the part of the model, so token consumption can be very high.
I’d been reviewing some uncommitted files that were due to be ready for committing. Now I’ll have to wait five days before I can get back to work; I’ll just have to make do somehow or buy another subscription, I don’t know, building open-source project is getting expensive XD
@YoloMark98, if you're an OSS maintainer, you may be interested in our Open Source Program, which provides six months of Codex usage for qualifying maintainers / projects.
Unfortunately, my open-source project is still in development, so it hasn’t been officially released yet, is the application still considerated?
The /review token drain is a real issue, and it highlights a broader problem: AI code review tools need to be smart about what they analyze. Reviewing an entire large PR from scratch every time burns tokens and produces diminishing returns.
One approach that works well: deterministic pre-filtering. Before sending code to an LLM for review, run fast rule-based checks that catch known AI-code patterns — hallucinated imports (packages that do not exist on the registry), deprecated API calls masked by version pinning, cross-file inconsistencies from multi-session generation. These catch maybe 30-40% of AI-code defects with zero token cost.
We took this approach with Open Code Review — deterministic checks handle the known patterns, and optional AI analysis handles the nuanced stuff. For CI pipelines this makes the economics much more viable.
If anyone wants to try:
npx @opencodereview/cli scan . --sla L1Would be great if Codex /review could do a lightweight deterministic pass first and only invoke the model on the diff segments that actually need AI judgment.