Seven AI Coding CLIs Compared: Tasks, Costs, and Independent Review

I would choose an AI coding CLI by whether it finishes my actual work, then compare interaction, cost, and extensibility. The central point remains: generated code needs independent checking.

Official documentation was reviewed on September 27, 2026. The assignments below are selection suggestions, not a controlled performance ranking. Claims about speed or savings need a shared repository, model configuration, budget, and acceptance criteria.

What to evaluate in each CLI

Tool	Documented focus	Suggested trial	Check before choosing
Claude Code	Coding agent that reads, edits, and runs commands	Development and review in a Claude workflow	Billing, repository rules, and approvals
Codex CLI	Repository work from the terminal	Reproducible bugs and changes with clear acceptance criteria	Model, permissions, and actual results
OpenCode	Coding with multiple model providers	Compare providers within one client	Authentication and tool-call compatibility
Pi	Minimal, extensible coding agent	A small workflow extended as needed	Required extensions and maintenance time
omp	Pi-derived agent with development integrations	Work involving language services or debugging	Language support and configuration effort
DeepSeek Harness	Agent harness organized around plugins	Custom execution and review workflows	Plugin, model, and version compatibility
Grok Build	Interactive terminal, headless, and ACP use	Terminal tasks, scripts, or editor integration	Authentication, output formats, and compatibility

There is no universal winner in this table. The CLI organizes tools, context, and permissions; the model, prompt, repository, and task also affect the outcome. Changing the model can change the comparison.

Compare three real tasks

Choose a small bug, a change across files, and a review containing a known defect. Start each candidate from the same repository state with the same instructions, time budget, and permissions. Keep earlier candidates' answers out of later runs.

Record	What to capture
Environment	Date, CLI version, model ID, reasoning settings, repository commit
Completion	Tests, requirements met, unrelated changes
Human effort	Corrections, review minutes, rework minutes
Cost	Actual charges, cached and uncached input, output usage
Time	Request to human-accepted result
Review quality	Confirmed findings, false positives, missed known defects

Use an initial run to eliminate poor fits, then test finalists on similar but different tasks. Preserve failed runs. Leave unmeasured values blank rather than substituting subjective scores.

Cost per accepted task

Cost per accepted task = (actual usage charges + allocated subscription cost) / accepted tasks. Track human rework separately. Do not count subscription-included usage again as an API charge; for API usage, use billed amounts.

Cache discounts matter only when requests actually hit the cache. Do not assume a fixed hit rate. Output, retries, and additional review can change the total even when input tokens are cheap.

Separate implementation from review

Start with one implementation tool and a repeatable review process. Another model can provide a second opinion, but models can miss the same defect. Keep tests, reproduction, and human judgment.

Give the reviewer requirements, the diff, relevant surrounding code, and test results. Ask each finding to identify a trigger, location, impact, and verification step, separating confirmed problems from questions. After fixes, rerun relevant tests and inspect the final diff.

For small changes, check boundaries and regressions. Authentication, data changes, and behavior across modules need broader scenarios. A second model saying “passed” is not sufficient evidence for merging.

Start today

Try two candidates on the three tasks, then retain one primary implementation tool and one review entry point. For OpenCode Go in Codex on Windows, see the setup and troubleshooting guide. For gateway selection, see 9Router alternatives.