How to pick an AI coding CLI for your repo

Pick an AI coding CLI by whether it can finish your repository tasks, then compare interaction, cost, and extension points. Generated code still needs an independent check.

How this page was checked (2026-10-06): we re-read official docs and related catalog/news pages. We did not run a comparative benchmark or the same coding task across tools, and we did not collect token bills.

Channel notes that help date the products: Claude Code 2.1.289, Codex CLI 0.160. Print your own --version when you start a log; desktop apps may exist even when a CLI is not on PATH.

You can confirm a CLI is on your PATH with:

claude --version
codex --version
opencode --version
pi --version
omp --version
grok --version

What each CLI is for (from official docs)

Tool	Official focus (2026-10-06)	Prefer it when	Check before you pay
Claude Code	Agentic coding: read, edit, run commands; terminal, IDE, desktop, web (overview)	You already work in a Claude subscription / Console workflow	Billing surface, repo rules, third-party provider support on CLI vs desktop
Codex CLI	Inspect, edit, run, automate from the terminal (Codex CLI)	Terminal-first repo work, `codex exec` / `codex review`	Sign-in method, sandbox/approval flags, model setting
OpenCode	Open-source coding agent; TUI, desktop, IDE; many providers (docs)	You want one client in front of several vendors	Each provider's auth and tool-call behavior
Pi	Minimal harness; extensions, skills, `pi -p` (pi.dev)	You will customize the harness instead of accepting a sealed product	Which extensions you must maintain
omp	Pi-derived agent with extra development integrations (oh-my-pi)	You need the bundled IDE-oriented extras on top of Pi	Language support and config cost vs stock Pi
DeepSeek Harness	Plugin-centered harness (GitHub)	You will assemble execution and review as plugins	Plugin, model, and version match
Grok Build	TUI, headless (`grok -p`), ACP (docs)	Terminal or scripted Grok work, or editor ACP	Browser login vs `XAI_API_KEY`

There is no universal winner. The CLI organizes tools, context, and permissions. The model, prompt, repository, and task change the outcome.

Shortlist by job (still not a ranking):

Stay in Anthropic's surfaces → start with Claude Code.
Stay in ChatGPT / Codex terminal → start with Codex CLI.
Swap vendors inside one TUI → start with OpenCode (or Pi if you will extend it).
Need Grok's TUI / headless / ACP → start with Grok Build.
Need a plugin harness around DeepSeek → read DeepSeek Harness first.

If you must share models across several of these agents, that is a gateway problem, not a CLI bake-off: see the magpie setup.

Three tasks, one log (run on your repo)

Use a small bug, a multi-file change, and a review that contains a defect you already know. Same git commit, same written instructions, same time box, same permission mode. Do not paste an earlier CLI's answer into the next.

Record	Capture
Environment	Date, `cli --version`, model id, reasoning setting, commit
Completion	Tests, requirements, unrelated diffs
Human effort	Extra prompts, review minutes, rework minutes
Cost	Invoice or subscription allocation; leave blank if unknown
Time	Request until a human accepts the result
Review	True findings, false positives, missed known defects

Vendor-documented starting points (they need auth and they change the tree):

claude "write tests for the auth module, run them, and fix any failures"
codex exec "Explain the project structure"
opencode
pi -p "List the top-level packages"
grok -p "Explain this codebase"

Use them only as starting points. Blank cells beat invented scores.

Cost per accepted task

Cost per accepted task = (usage charges + allocated subscription cost) / accepted tasks. Track rework minutes separately. Do not double-count subscription-included usage as API spend.

Cache discounts count only on requests that actually hit cache. Cheap input tokens do not cap the bill if output, retries, or extra review grow.

Split implementation from review

Keep one implementation CLI and a repeatable review path. A second model can comment; it can also miss the same bug. Tests, reproduction, and a human still decide.

Give the reviewer the requirement, the diff, nearby code, and test output. Each finding needs trigger, location, impact, and how to verify. After fixes, re-run the relevant tests and read the final diff.

Windows Codex + OpenCode Go: setup guide. Gateways: 9Router alternatives.