Short answer
If you need seamless in‑IDE, line- and function-level completions that speed everyday coding, pick GitHub Copilot. If you need multi-file reasoning, complex refactors, PR summarization, or organization-level data/privacy controls, pick ChatGPT Enterprise. Many teams run both: Copilot for day-to-day coding, ChatGPT Enterprise for higher-level design, onboarding, and review automation.

Recommendation
- For daily developer productivity and the lowest friction to ship small changes: GitHub Copilot. It integrates into editors, offers completions from contextual code, and reduces boilerplate.
- For review automation, multi-step transformations, architecture prompts, or strict enterprise data controls: ChatGPT Enterprise.

Decision criteria (use these to choose for your team)
- Workflow stage: execution/line-level → Copilot. Cross-file refactor, design, or PR analysis → ChatGPT.
- IDE integration need: critical → Copilot (native plugins). ChatGPT requires plugins or API wiring into tools.
- Accuracy vs reasoning: single-file completions → Copilot is fast and practical. Complex reasoning and explanation → ChatGPT wins.
- Security & compliance: strict enterprise controls or “no-training-on-customer-data” requirement → ChatGPT Enterprise. For public-code training concerns and IP policy reviews, evaluate Copilot for Business safeguards.
- Team size & budget: per-seat Copilot is often cheaper for individual devs; ChatGPT Enterprise costs scale differently but adds org features and admin controls.
- Impact on review throughput: measure (see checklist) — Copilot increases velocity on small PRs; ChatGPT can reduce review time for larger PRs (automated summaries, suggested changes).

Benchmarks to run (practical, measurable tests)
1) Accuracy: generate N tasks (10–50) representative of your codebase. Measure unit-test pass rate and required manual edits.
2) Security: run SAST and dependency scanners on AI-generated code and compare the number of flagged findings per 1,000 lines.
3) IDE integration: time-to-first-usable-suggestion (seconds), acceptance rate of suggestions, and context window effectiveness (multi-file context vs single-file).
4) Review throughput: for a 2–4 week pilot, measure mean time-to-merge, average review comments per PR, and reviewer hours saved.
5) False-positive/bug rate: track bugs introduced by AI suggestions after merge for 30–60 days.

Practical checklist for a pilot
- Pick a representative cohort (5–20 devs) and instrument metrics (time-to-merge, review comments, test pass rate).
- Configure data controls and scanning: enable SAST, secret scanning, and policy gates for generated code.
- Define acceptance criteria up front (e.g., >=30% reduction in boilerplate time or <X security findings per 1000 LOC).
- Run parallel experiments: same tasks, one group uses Copilot, one uses ChatGPT (with templates/plugins), one control group.
- Collect qualitative feedback: dev satisfaction, perceived usefulness, and friction.
- Decide after 4–8 weeks and scale with guardrails (pre-commit checks, code review rules).

Best-for / Avoid-if
- Best-for Copilot: fast autocompletions, boilerplate, in-editor speedups. Avoid-if you need enterprise-level prompt-based workflows or centralized PR summarization.
- Best-for ChatGPT Enterprise: cross-file reasoning, team-level automation, security/compliance controls. Avoid-if your primary need is instant in-editor line completions and minimal context switching.

Final note
Budget, skill level, and the complexity of the codebase matter: smaller teams focused on shipping features often get faster wins with Copilot; larger orgs with compliance needs and heavy review workflows often benefit more from ChatGPT Enterprise or a hybrid approach.

Compare ChatGPT and Gemini