codex - 💡(How to fix) Fix `codex-auto-review` in OTel token usage metrics is hard to map to official model pricing [1 comments, 2 participants]

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openai/codex#20981Fetched 2026-05-05 05:55:02
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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.

Root Cause

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.

Code Example

{"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"
}
RAW_BUFFERClick to expand / collapse

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.

extent analysis

TL;DR

The Codex team should clarify the meaning of model="codex-auto-review" in OpenTelemetry token usage metrics to ensure accurate cost reporting.

Guidance

  • Verify the official OpenAI model names and their corresponding pricing documentation to confirm that codex-auto-review is not a recognized model.
  • Investigate if codex-auto-review is an internal Codex routing alias or a stable public label that can be mapped to a billable model identity.
  • Consider reaching out to the Codex team for clarification on the intended use of model="codex-auto-review" in token usage metrics.
  • If a mapping is possible, update the pricing table to include codex-auto-review with its corresponding billable model ID and price.

Notes

The issue lacks information on the Codex team's intentions behind using model="codex-auto-review", making it difficult to provide a definitive solution.

Recommendation

Apply workaround: Wait for clarification from the Codex team on the meaning of model="codex-auto-review" before making any changes to the pricing table or dashboard.

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