codex-reasoning-level-calibration

name: codex-reasoning-level-calibration description: "Calibrate the OpenAI Codex reasoning-level dial before recommending a model upgrade. Maps task type, failure mode, and budget to the right reasoning effort level — ensuring developers exhaust the reasoning dial on the current model before paying for a bigger SKU. Domain-specific to the Codex reasoning API."

Skill: Codex Reasoning-Level Calibration

The Codex reasoning-level dial is a cost-effective lever that most developers skip. Before upgrading to a bigger Codex SKU, try raising reasoning effort on the current model. This skill ensures the reasoning dial is calibrated correctly before a model upgrade is recommended.

When to reach for this skill

Output quality is insufficient on the Codex default model at default reasoning.
The developer is considering upgrading to a frontier Codex model.
The task is latency-tolerant (background runs, supervised agentic work, large refactors).

Do not apply to inline completions or interactive chat where latency is the binding constraint — raising reasoning effort increases latency in a way users feel immediately.

Step 1 — Classify the failure mode

Different failure modes call for different reasoning levels:

Failure mode	Signal	Reasoning-dial response
Shallow logic errors	Plausible-but-wrong code; misses obvious constraints	Raise reasoning to medium-high
Missing context integration	Ignores established patterns in the codebase	Raise reasoning + improve prompt context
Incomplete multi-step plans	Stops early on multi-file or multi-step tasks	Raise reasoning to high; or decompose the task
Wrong tool/API choice	Picks a deprecated or incorrect API	Raise reasoning + provide explicit API list
Nondeterministic quality	Excellent sometimes, poor other times	Raising reasoning reduces variance; test at high

Step 2 — Map to reasoning level

OpenAI Codex exposes reasoning effort as a dial (exact parameter name and values: verify-at-use against the current API — (verify-at-use — 2026-06)):

Level	Cost delta	Latency delta	Use when
Low / off	1x baseline	Lowest	Autocomplete, quick edits
Medium	Moderate increase	Moderate	Most supervised coding tasks
High	Larger increase	Higher	Hard multi-file tasks, long agentic runs
Max	Highest	Highest	Hardest tail; before considering a model upgrade

Rule: exhaust the reasoning-level dial on the current model before upgrading the model. A model upgrade multiplies the per-token cost; raising reasoning effort increases cost more modestly.

Step 3 — Test the calibration

Run the failing task at the next reasoning level up. Accept the result if:

The failure mode from Step 1 is resolved.
Latency is still acceptable for the task's interactivity requirement.
The cost delta is within the task's budget.

If all three hold, the new reasoning level is the recommendation — not a model upgrade.

Step 4 — When reasoning dial is at max and still failing

Document:

Task description and why it's failing at max reasoning.
That reasoning has been exhausted on the current model.
Whether task decomposition (splitting into smaller steps) could substitute for a model upgrade.

Only after confirming that decomposition won't close the gap does this skill hand off to codex-model-strategist with a recommendation to upgrade the model SKU.

Pitfalls

Jumping straight to a model upgrade because "the default isn't working" — the reasoning dial is almost always cheaper.
Applying high reasoning to interactive chat — the latency impact is felt immediately by users.
Treating "reasoning level" as a binary (on/off) — it is a spectrum; test incrementally.
Forgetting to document that the dial was exhausted before recommending an upgrade — the next engineer will otherwise repeat the calibration.