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First identify what was deprecated
“Deprecated” can mean a model was removed from one product, an API endpoint is being retired, or a review app has shut down. These are not interchangeable. GitHub maintains both a current Copilot supported-model list and model retirement history; OpenAI publishes a separate API deprecation schedule. An API retirement does not, by itself, establish that the same model has disappeared from every app or coding product.
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Check the notice for the specific product surface, plan, organization policy, and account type you use. The available models can differ across those contexts, and a provider’s live model list is a better final check than an older migration announcement.
A dated Copilot migration example
In its August 31, 2026 announcement, GitHub said selected models would be deprecated across most Copilot experiences starting September 1, 2026. Its suggested replacements were:
| Deprecated model | Suggested replacement in GitHub’s announcement |
|---|---|
| Gemini 3.1 Pro | Gemini 3.7 Flash |
| Claude Sonnet 4.5 or 4.6 | Claude Sonnet 5 |
| Claude Opus 4.5 or 4.6 | Claude Opus 4.7, 4.8, or 5 |
GitHub noted that Claude Sonnet 4.6 remained available to individual subscribers on annual plans. That exception illustrates why you should verify the announcement against your account and the dated migration notice, then confirm the live roster in GitHub’s supported-model documentation. As of October 4, 2026, that documentation lists model families including GPT-5.3-Codex, Claude Sonnet 5, Claude Opus 5, and Gemini 3.8 Flash; it is a current snapshot, not a promise of access in every Copilot experience.
Choose whether to replace the model or the review workflow
If your review tool still works and only its model changed, start by checking supported models and organization controls within that product. If the review app itself is gone, compare alternatives by where they can review code and how they start a review. A familiar model name does not guarantee the same pull-request integration, permissions, or billing.
Keep GitHub Copilot for pull-request reviews
GitHub Copilot’s code-review feature reviews pull requests, identifies issues, and suggests fixes. GitHub lists it as available on paid plans and documents support across several product surfaces. For organization-provided users, an administrator may need to enable the review option. See GitHub’s Copilot code review documentation for current availability and setup.
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Use Claude Code Review
Anthropic documents Claude Code Review for pull-request analysis and inline findings, with review triggers that can be configured manually or automatically. Check its current setup instructions and billing conditions before moving a team workflow: the existence of a review feature does not establish that its plan limits or costs match another product. See Anthropic’s setup guide.
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Use Codex for documented PR review workflows
OpenAI documents a Codex workflow for finding pull requests, inspecting changes, and working through findings. Its help page describes GitLab merge-request review as a preview and says GitLab cloud code reviews are unavailable, so do not assume universal repository-host support. Check the current Codex pull-request review instructions and Codex product information for your intended setup.
Check the Google product edition before choosing Gemini Code Assist
Google Cloud documents Gemini Code Assist for automated code reviews and pull-request summaries on GitHub. Separately, Google says the consumer version of Gemini Code Assist on GitHub was deprecated June 18, 2026, and shut down July 17, 2026. Do not treat the retired consumer app as the Google Cloud offering: verify the available edition, account, and region in Google Cloud’s GitHub review documentation and Google’s feature deprecation notice.
Compare the workflow before choosing by model name
For a model replacement inside an existing tool, availability and your organization’s model policy are usually the first checks. When changing review products, evaluate the integration and operating model as well. Confirm each detail in the relevant provider’s current documentation.
- Repository host and permissions: Verify that the service supports your GitHub or other repository setup, and determine what access it needs.
- Review trigger: Find out whether reviews are requested manually, started automatically, or can use either approach.
- Feedback format: Check whether findings appear inline on changed lines, in a summary, or both.
- Model controls: Establish which models are available in your particular plan or product surface and whether an organization can restrict them.
- Organization policy and data handling: Review the relevant administrative controls and provider terms before connecting a team repository.
- Usage and billing: Check plan limits, credits, and billing conditions directly. Model names alone do not show whether two services cost the same or offer comparable allowances.
No controlled head-to-head benchmark or comparable current-price survey establishes one of these options as universally most accurate or cheapest. Choose based on the workflow you need and validate performance on code your team can safely review.
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Keep human review in the loop
AI findings can help surface issues, but they do not replace a maintainer’s judgment or security review. GitHub’s Supported AI models in Copilot documentation advises: “Users should always carefully review and validate code, including code security, using a range of models and with a thorough human review before incorporating suggestions into production.”
A 2026 arXiv preprint, “Not All Agents Are Equal,” examined 37,623 provenance-labeled pull requests across 2,807 GitHub repositories, dated December 2024 through July 2025. The study authors report that 6.1% of Codex-attributed PRs were reverted, compared with 11.5% of the human comparison, and that 14.5% of Devin-attributed PRs were reverted. They also report a lower likelihood of a measured security smell for agent code pooled across vendors (odds ratio 0.63). These are observational findings about generated pull requests and post-merge outcomes—not a controlled comparison of code-review services or proof that one replacement is best.
The same paper reports a 12.6-hour median wait to first human review for Claude Code PRs. That is a measure of review timing for agent-authored pull requests, not a test of Claude Code Review’s defect detection.
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