Detect whether the model actually used by Codex for responses matches the selected model, and identify silent downgrades or model switches. https://github.com/kiyoakii/is-gpt-nerfed… Codex shrinkage detector on macOS, residing in the menu bar, to determine if the selected model is truly being used for responses. The plugin reads Codex session records round by round, marking model switches, reduced inference strength, hidden models, and small context windows with zero token overhead.

kiyoakii/is-gpt-nerfed

Source: https://github.com/kiyoakii/is-gpt-nerfed

English · 简体中文

You pick a model in Codex. This tells you whether that model is actually the one answering. If it isn’t, you get this:

🎉 Congrats! You've been nerfed! You asked for gpt-6-astra; the fingerprint says gpt-5.6-luna (91%). Enjoy the discount you didn't ask for.
_{Example output, not a real verdict.}

_{The panel, and a session opened in place. Sample data, not real sessions.}

_{All clear · Suspicious · Nerfed}

What it checks

Everything runs on your Mac. Nothing is uploaded.

Codex records, for every turn, which model and reasoning effort it asked for. The plugin reads those records after each turn and flags what changed without you changing it: a model swap, a lower reasoning effort, a hidden internal model such as gpt-reserve, a smaller context window. A move to a newer or larger model is reported as well, since a rollout can go either way. This costs no tokens.

On a schedule, the plugin also forks your session three times, ephemerally, through the same app-server call the desktop uses for a side chat, with the session’s own model and reasoning effort. Each fork is asked for about 300 “random” numbers. A language model picks random numbers with a bias that is characteristic of the model. ModelTrace’s calibrated bank turns the three answers into a fingerprint (100 % accuracy with three answers in cross-validation), and the verdict compares that fingerprint with the model you selected:

A mismatch reaches you as a macOS notification, a message in the session, and a red face in the menu bar. A match is not announced.

Install

macOS 26. One line in Terminal installs the latest release and opens it:

curl -fsSL https://raw.githubusercontent.com/kiyoakii/is-gpt-nerfed/main/install-app.sh | sh

You can also download the disk image from Releases and drag IsGPTNerfed to Applications. The app is not notarized yet, so macOS blocks that first launch until you allow it under System Settings → Privacy & Security.

Click the face in the menu bar and press Install. That registers the bundled plugin with Codex and trusts its hooks. When a newer release is out, the footer says so and a notification arrives once; click it and the app downloads the new build, checks its checksum, replaces itself and relaunches.

Without the app, on any macOS:

git clone https://github.com/kiyoakii/is-gpt-nerfed ~/is-gpt-nerfed && cd ~/is-gpt-nerfed && ./install.sh

Say yes when it asks to trust the hooks. Codex runs no hook you have not trusted, and it does not tell you.

Requires a Codex with plugin hooks (desktop app or CLI; tested on 0.154) and the system python3. ./uninstall.sh removes everything.

Using it

The macOS interface supports English and Simplified Chinese, following the system’s preferred languages with English as the fallback. This includes app-generated evidence, progress messages and diagnostics shown by the app. User session titles, model IDs, reasoning-effort values and unrecognized external diagnostics keep their original text. Stored records, CLI output, notifications and ModelTrace’s calibrated probe prompts are unchanged.

Every session you work in is probed in the background after 30 minutes of activity: the hooks run the schedule after each turn, and the menu bar app catches a session that goes quiet right after its due time. To probe a session right away, say $is-gpt-nerfed in it.

The menu bar panel lists the sessions of the last 48 hours with their last verdict. Click a session for its report (fingerprint, earlier probes, evidence) and right-click for actions. Each session has Probe and Retry, and the Fresh session row probes a brand-new session to show what a new session gets right now.

From a terminal: nerfed probe now (pick a session), nerfed report, nerfed explain <probe>, nerfed log --since 2h.

Settings are in the app, or nerfed config set <key> <value>:

Accounts

Sessions are shared between Codex accounts, and a downgrade may be tied to the account rather than the session. Every probe is therefore tagged with the signed-in account (a hash, never the id). After you switch accounts, older verdicts show as “another account” and those sessions are probed again.

Limits

• “The model you selected” is the model Codex asked for. If the server swaps the weights and keeps the name, only the fingerprint or a smaller context window can show it.
• The bank is closed-set: a model outside it is mapped to its nearest look-alike.
• A probe costs three short answers on your account. A fork that has not answered within five minutes is replaced once, so a slow model does not drop out of the sample.
• A model or effort change made through Codex’s own settings is shown as a question (“was that you?”), because the plugin cannot tell whether you or Codex changed it.
• The probes run in a private app-server process that identifies itself as the client it checks for (the desktop app, or the CLI), because the service may route by client. Anything else that differs between the desktop’s own connection and the probe’s is invisible to it.

Privacy

The plugin reads ~/.codex (session records, the models cache, and auth.json only for an account hash and a masked e-mail) and writes to ~/.codex/is-gpt-nerfed (probes, verdicts, log.jsonl). The forks are ordinary Codex inference under your account. The only network request of its own is one to GitHub every ten minutes for the latest release tag, while the app is open; switch it off in Settings and it makes none.

Credits

ModelTrace (xqy2006, MIT) for the fingerprint bank, scorer, prompts and the fork-and-verify sequence; hlwy-ai-checker for the random-number idea; simple-term-menu (MIT) for the session picker.

MIT license. Internals, build and contribution notes: docs/DEVELOPMENT.md.