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Before switching AI code reviewers because a free tier is changing, compare them on the same pull requests and measure both useful findings and false alarms. A shared benchmark such as GitHub’s ReviewBench can help screen candidates, but your own representative changes are needed to judge fit for your repository.
Start with a controlled comparison
An AI reviewer is more than its underlying model: its instructions, repository context, tools, and configuration can all affect what it reports. To make a migration decision you can defend, compare the complete setups under the same conditions rather than relying on a demo or a single aggregate score.
ReviewBench is an open, reproducible benchmark that pairs pull requests with human-reviewed reference findings. Its repository includes a 25-task test set, a 219-task full corpus, public corpus materials, and instructions for local runs. The selected tasks span languages, repository and change sizes, finding categories, and severities. GitHub says the 219 public pull requests cover 19 languages and align with GitHub-wide language and repository-size distributions. That makes the benchmark useful for screening, not a substitute for testing your own work.
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Build a migration diary before running tools
Write down the setup so a result remains interpretable if you repeat the test after a plan, model, or configuration changes.
- Candidate and access: reviewer name and version, plan or tier, model selection if exposed, and any usage limits relevant to the test.
- Configuration: review instructions, repository context and retrieval settings, tool access, and available temperature or effort controls.
- Test identity: repository commit, exact pull-request revisions, candidate list, and date of each run.
- Evaluation rules: what counts as valid, actionable, duplicate, severity-appropriate, or missed; define these before seeing results.
- Operational measures: latency, failed runs, human review time, usage, and cost as actually measured under the tested plan or billing arrangement.
Keep credentials, context, and settings consistent where possible. If a candidate requires a different harness or cannot access the same context or tools, record that difference; it is part of the workflow being compared.
Choose pull requests that resemble your work
Use a small set to debug the harness
Begin with a small smoke set to confirm that every candidate can access the intended patch and context, complete a review, and return findings in a form your evaluators can score. Use this stage to fix operational problems, not to declare a winner.
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Hold out a representative evaluation set
For the actual comparison, choose a larger set of immutable pull-request revisions reflecting your team’s languages, change sizes, and risk categories. Include routine and difficult work; selecting only easy examples or striking bugs can distort the result. Keep the held-out set separate from prompt tuning where practical, so iteration on the smoke set does not quietly turn into fitting the evaluation.
Run every candidate on the same revisions and provide equivalent repository context. If you change instructions or settings during a run, log the change and treat that configuration as a distinct tested setup.
Score both useful findings and noise
GitHub defines precision as the proportion of surfaced issues that are valid, and recall as the proportion of known valid issues the reviewer finds. Precision speaks to noise: low precision means more surfaced findings are invalid. Recall speaks to coverage: low recall means more known issues are missed. Neither alone answers whether a reviewer is useful to your team.
Rank #3
| Measure | What it tells you | When it helps |
|---|---|---|
| Precision | Among surfaced findings, the share that are valid. | Useful when false positives consume reviewer time or undermine trust. |
| Recall | Among known valid findings, the share the reviewer identifies. | Useful when broad issue coverage is the priority. |
| F1 | A combined measure that balances precision and recall equally. | Useful when neither noise nor missed issues should dominate the comparison. |
| Fβ | A combined measure that lets the team weight precision or recall more heavily. | Useful when the cost of false alarms and missed issues is demonstrably asymmetric. |
ReviewBench also reports grounded and augmented precision and recall, and lets readers explore outcomes by severity and category. Use those slices rather than letting one overall score conceal a weakness in a risk area your team cares about. A reviewer that performs well on low-severity style findings may not be the right choice if your priority is security or correctness issues.
Have evaluators who do not know which tool produced each finding apply the rubric. Compare valid and actionable findings, duplicates, severity appropriateness, and known issues missed. Report the sample size alongside the scores. A small sample is directional evidence, not a decisive ranking; inspect the underlying examples and uncertainty rather than treating a decimal difference as proof.
Inspect disagreements and workflow effects
Review findings on which candidates disagree, plus findings that all candidates miss. Check whether a purported issue is grounded in the patch and repository context, whether it is actionable, and whether its severity is justified. Record the reason for each judgment so you can distinguish an actual reviewer weakness from a rubric ambiguity or context gap.
GitHub notes that ReviewBench movements are checked against online experiments, but benchmark results still do not guarantee production performance in your repository. A score can change when the harness, instructions, context retrieval, or grader changes. Attribute conclusions to the complete setup you tested, then validate on representative work before expanding use.
Instructions can matter as much as the selected tool. In one reported GitHub example, a migration initially increased cost and reduced issue detection; after the team rewrote instructions for how a reviewer reads a pull request, it reported roughly 20% lower average review cost while maintaining the same review quality. This is a result for that specific Copilot workflow adjustment, not a saving to expect from other migrations.
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Check free access and cost as of the cutover
A “free” label does not establish that code review is included, that access is equivalent between plans, or that the same billing rules apply to individuals and organizations. Check the service’s current plan terms and your organization’s policy before setting a cutover date or estimating cost.
Best Value
As listed on GitHub’s live plan page when accessed in 2026, Copilot Free includes 2,000 completions and 50 chat requests. The page separately says code review is not included in the Free individual plan; organizations may enable pull-request code review for users without a Copilot license under specific policies, with usage billed in GitHub AI Credits. These are current page details, not evergreen limits; verify the Copilot plans page and applicable organization policy at the time you decide.
Compare measured usage and cost per review for the tested plan, not just the headline tier. Also note latency, failures, and how often a human accepts, rejects, or corrects a finding. A lower nominal price may not be a lower operating cost if the setup produces more noise or needs substantial manual correction.
Make the cutover reversible
Use the benchmark to narrow candidates, then stage the change on a limited set of repositories or pull requests with human review retained. Track accepted findings, rejected findings, missed-issue reports, failures, latency, and actual usage against the baseline. Set a review point and keep a rollback path; expand only when the observed tradeoff fits the team’s risk tolerance and operating constraints.
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