`codex-auto-review` in OTel token usage metrics is hard to map to official model pricing

Summary

When exporting Codex metrics through OpenTelemetry and visualizing them in Prometheus/Grafana, some token usage metrics include model="codex-auto-review".

This is confusing for cost dashboards because codex-auto-review does not appear to be an official public OpenAI model name in the pricing documentation. The token usage metric has the token counts needed for accounting, but the model label cannot be directly joined with a price table keyed by official/billable model IDs such as gpt-5.4, gpt-5.4-mini, or gpt-5.3-codex.

Example metric

{"codex.turn.token_usage_sum",
app.version="0.128.0-alpha.1",
auth_mode="Chatgpt",
env="macos",
model="codex-auto-review",
originator="codex_vscode",
os="Mac_OS",
os_version="26.4.1",
scope.name="codex",
scope.version="unknown",
service.name="codex-app-server",
service.version="0.128.0-alpha.1",
session_source="subagent_guardian",
telemetry.sdk.language="rust",
telemetry.sdk.name="opentelemetry",
telemetry.sdk.version="0.31.0",
tmp_mem_enabled="false",
token_type="cached_input"
}

Why this matters

For a Grafana dashboard that estimates Codex spend from token metrics, the natural approach is to aggregate token usage by (model, token_type) and multiply it by a pricing table with the same labels.

That breaks when the metric uses codex-auto-review, because it is not clear which official model or billing rate should be used. As a result, dashboard authors have to either drop these samples, guess a mapping, or assign a fallback price, all of which can make cost reporting inaccurate.

Request

Could the Codex team clarify what model="codex-auto-review" means in OTel token usage metrics, especially for cost or credit accounting?

In particular, it would be helpful to know whether this label represents an internal Codex routing alias, a stable public label, or the actual billable model identity users should rely on when building usage dashboards.