Short answer
Use both, but for different roles: GitHub Copilot (editor-first) for fast, context-aware inline suggestions during development; ChatGPT (API/enterprise) for CI-level PR summaries, suggested fixes, and natural-language review comments—only if you add strict redaction, sandboxed validation, and a human-in-the-loop.

Recommendation
- Developer workflow: rely on Copilot to speed local reviews and suggest minimal, testable fixes in the editor. It’s optimized for code completions and uses immediate file context.
- CI/automated PR review: use ChatGPT (or an LLM API) to generate reviewer-like comments and proposed patches, but treat outputs as proposals. Always validate suggested fixes with automated tests and static analysis before posting or committing.

Decision criteria (pick the most important for your team)
- Privacy/security: If you must avoid sending repo content off-site, neither SaaS is perfect; prefer enterprise plans with data-use guarantees or on-prem models. Redact secrets and minimize context size.
- Accuracy/precision needed: For precise code edits, Copilot in-editor is usually more precise; ChatGPT can explain trade-offs better and craft multi-file change proposals but may hallucinate code.
- Integration effort & automation: ChatGPT APIs are easier to integrate into CI pipelines for automated comments; Copilot is more IDE-integrated and less suited to CI automation.
- Cost & scale: Copilot pricing is per-seat; ChatGPT API costs scale by usage—high-volume CI checks can be costly.
- Team size & workflow stage: Small teams or individuals benefit more immediately from Copilot. Larger orgs that want centralized CI automation and standardized review messaging will find ChatGPT API more flexible.

Best-for / Avoid-if
- Best for Copilot: developers who want rapid, local code fixes and inline suggestions tied to current file context. Avoid if you need automated PR-level narrative or multi-file patch orchestration.
- Best for ChatGPT: generating human-friendly review comments, summarizing large diffs, and proposing multi-file fixes in CI. Avoid if you cannot guarantee secure handling of repo data or you need 100% deterministic, compile-safe fixes without human review.

Practical checklist to deploy LLM-based PR reviewers safely
1) Define scope: which repos/branches get automated reviews and which authors get auto-comments.
2) Redact sensitive data: remove tokens, passwords, and limit file chunks sent to the model. Use heuristics to strip secrets before API calls.
3) Provide minimal context: include only the diff, file paths, failing CI output, and relevant tests to reduce hallucination risk.
4) Validate suggestions: automatically apply suggested patch to a sandbox branch, run full test suite and linters, and reject any patch that breaks tests.
5) Confidence scoring: record model-assigned confidence and require human approval below a threshold.
6) Audit logs & escalation: log suggestions, prompts, and model replies; provide easy escalation to a human reviewer.
7) Rate limits & cost control: cap requests per PR and use retries/backoff to control costs.
8) Policy & training: train reviewers to treat model output as suggestions, not authoritative fixes.

When the right answer depends
- Budget: high-volume CI favors Copilot per-seat vs API costs—run a cost projection.
- Skill level: less experienced teams need stricter validation and human review.
- Workflow stage: early-stage projects can use more permissive automation; mature codebases should be conservative.

If you want, I can sketch a simple CI flow: diff -> redact -> prompt -> sandbox apply -> run tests -> comment only on passing patches. For implementation help, check ChatGPT enterprise/API options for secure integrations.

Compare ChatGPT and Gemini