Keep model IDs and review prompts in version-controlled application configuration, monitor the provider’s retirement notices, and test a supported replacement before switching. A deprecation announcement is not necessarily an immediate shutdown: the announced shutdown date is when access ends, and the timeline varies by model and provider.
Deprecation is not the same as shutdown
OpenAI defines a model as deprecated once retirement has been announced; access ends on its shutdown date. Its documentation uses “sunset” and “shut down” interchangeably for the point when the service is no longer accessible. Dates and recommended replacements are model-specific, so check the provider’s current notice for the exact identifier your workflow calls: OpenAI API deprecations.
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OpenAI’s current API policy gives generally available models at least six months’ notice and specialized variants of generally available models at least three months. Preview models may have much shorter notice; OpenAI gives two weeks as an example. These are OpenAI policy categories, not a guarantee for every provider, and safety or compliance concerns can shorten notice. Treat the dates in the live deprecation notice—not a general policy estimate—as the migration deadline.
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Inventory every dependency
Find the model identifier and provider endpoint, then trace how the review service invokes them. Record prompt templates, structured-output assumptions, tool calls, and model-specific parameters. Note which repositories and pull-request events use the service so a change is not tested in only one path.
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Track official notices
Subscribe to provider email and changelog notices, and put each relevant announcement and shutdown date on the team’s maintenance calendar. OpenAI says affected customers are notified and lists retirements on its deprecation page. Recheck the provider’s notice as the date approaches because schedules and recommendations can change.
Confirm availability in the product surface you use
A model may be available through a provider’s API but not through an integrated coding product, or vice versa. Check support for the exact API, IDE, or code-review service in use. For GitHub Copilot, consult GitHub’s supported models reference, including its retirement history; if availability is unclear, use the product vendor’s support information rather than assuming the API’s model list applies.
Keep prompts and behavior configuration reviewable
Store reusable prompt content in application code or an equivalent version-controlled system. That lets changes go through normal review, testing, and deployment practices instead of leaving critical behavior in an untracked managed setting. For OpenAI-managed prompts, its migration guidance says: “To migrate away from Prompts in the OpenAI API platform, move the prompt content out of the managed prompt object and into your application code.” See OpenAI prompt engineering guidance and OpenAI prompt migration guidance.
Evaluate a replacement on real review cases
Build a representative evaluation set
Use historical pull-request examples that are anonymized or otherwise approved for this purpose. Include routine changes as well as important risk areas, and record expected findings and known false positives. OpenAI recommends using notice time to evaluate replacements and test application behavior; it does not prescribe a code-review benchmark or universal pass threshold.
Run a like-for-like comparison
Where both models remain available, run them against the same cases with the same review criteria. Assess:
- Whether useful issues are found, including important expected findings.
- Whether comments are actionable and grounded in the change.
- The false-positive burden on developers.
- Response failures and reliability in the workflow.
- Latency and cost under the team’s expected usage.
Set acceptance thresholds to match the team’s risk and review volume. There is no universal threshold or sourced code-review-specific migration success rate that can substitute for that decision. A replacement returning plausible comments is not evidence by itself that review quality has been maintained.
Switch safely and preserve a fallback
- Implement the replacement behind configuration. Keep the model identifier and relevant behavior settings in an application-controlled configuration point so the route can be changed without scattering model names across the codebase.
- Roll out gradually if the architecture permits. Use a configuration flag, a staged repository rollout, or shadow comparison. Monitor review-job failures and the evaluation measures established by the team.
- Keep rollback time-bounded. Retain the previous route only while the provider still serves it and its use remains allowed. Make sure failed automated review jobs have an alarm and a human-review fallback.
- Remove obsolete assumptions after cutover. Delete the retired identifier and no-longer-valid parameters, update runbooks, and keep the evaluation set for the next model change.
What to compare when several replacements are supported
| Decision area | What to verify |
|---|---|
| Availability | Supported in the exact API or integrated product surface used by the workflow. |
| Review usefulness | Expected issues found and findings that developers can act on. |
| False positives | How much unnecessary review work the model creates. |
| Reliability | Failure behavior and whether review jobs complete consistently. |
| Performance and cost | Latency and cost for the team’s own representative cases and expected volume. |
These are evaluation axes, not published head-to-head results. Provider notices establish retirement timing and may recommend a replacement, but only testing against your own approved pull-request cases shows whether that option fits your review workflow.
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