Run a repository trial before choosing Gemini CLI or Claude Code

Gemini CLI is Google's open-source AI agent for the terminal. Claude Code is Anthropic's coding agent, also used from the terminal. Both read a repository, edit files, run commands and connect to outside tools, and both change quickly enough that any feature-by-feature comparison is out of date within weeks. The comparison worth making is how each behaves on your code, under your rules, with your reviewers. This guide sets out a trial you can run in a few days, organized around four questions: the permission model, extensibility, the cost model and the review workflow. If you are still deciding between a terminal agent and an AI editor, start with terminal agent or editor agent.

Set up a fair trial

A demo on a toy project tells you little. Use your own repository and real work:

1. Pick five to eight tasks from your backlog that cover different shapes: a bug with a known reproduction, tests for an untested module, a small feature, a dependency upgrade, a refactor across several files, and one question with no edit, such as explaining how a request flows through the code.
2. Write acceptance criteria for each task before running anything: which tests must pass, what must not change, which files are out of scope. The note on writing acceptance criteria first has a format you can reuse.
3. Start every run from the same commit on a fresh branch or worktree, so neither tool benefits from the other's leftovers.
4. Give both tools equivalent project instructions. Each has its own convention for instruction files; put the same content in both.
5. Record everything: the transcript, every command run, every permission prompt, the final diff, test results, and the minutes you spent reviewing.
6. Grade blind where you can. Ask a colleague to review the diffs without knowing which tool produced them.

Permission model: watch what happens without asking

Settings pages describe intentions; a trial shows behaviour. For each tool, observe:

• What it does on a fresh install before you change any setting.
• How fine-grained approvals are: per command, per tool, per session.
• Whether you can allowlist routine commands and deny risky ones, and whether those rules live per user, per project in a committed file, or under central management.
• Whether commands can run inside a sandbox or container, and how much setup that takes.
• Whether an approve-everything mode exists and whether your team can disable it.
• How it treats files outside the project directory and secrets inside it.

Include one task that tempts the agent past your policy, for example one that would be easier with a new dependency while installing packages is set to ask. See whether it asks, works around the restriction, or stops and explains. Start agents read-only describes the opening policy worth testing against.

Extensibility: what you can add, and who controls it

Both tools can be extended. Check how each connects to MCP servers, whether it supports custom commands or reusable instructions, whether it offers hooks that run your own scripts at defined points, and how all of that is shared through the repository so every developer gets the same setup. Scope matters as much as reach: an extension enabled globally is available in every project, including the ones where it should not be.

Source availability is part of extensibility. Gemini CLI publishes its code in an open repository; for any tool, check whether the source is available and under what licence, because reading it is the most direct way to know what runs on your machine. For the broader trade-offs between extension points, see MCP, skills or CLI tools.

Cost model: price a finished task

Terminal agents are billed in several ways: a free allowance tied to a personal account, a subscription, metered usage through an API key, or an organization's cloud account. The same trial can look cheap under one model and expensive under another, so record usage yourself rather than estimating from a price page. Questions to answer:

• Which billing route applies to your team, and who owns the account?
• What happens at a limit: does the tool stop cleanly, slow down, switch to a different model, or fail halfway through an edit?
• Can you see usage per session and set a hard spending cap?
• What did each completed task cost, counting failed attempts and retries?

Cost per accepted change is the figure that maps to value. A cap is a safety device, not a budget; putting a ceiling on agent spend explains the difference.

Review workflow: the hours that do not show on the bill

The largest cost in agent-assisted coding is usually the reviewer's attention. During the trial, note how each tool presents its changes, whether it can roll back its own edits, whether it commits on its own initiative, whether it runs tests without being asked, and whether you can resume an interrupted session. Then look at what remains afterwards: can the transcript be exported and attached to a pull request, so the next reviewer sees which commands ran and why? What an agent audit trail should record lists the minimum worth keeping.

A scoring sheet

Agree on weights before the first run, and fill in the sheet from your records rather than from memory.

Run the set twice on different days. Agents are not deterministic, and a single run can flatter either tool.

Where GoCux fits

GoCux is not a coding agent and does not rank them. Its benchmarks page makes the same argument as this guide: judge the work, not only the answer. Where agent work has to be reviewable by a team rather than one developer, a work order carries a goal, a scope, acceptance criteria and bounds such as wall time and the number of tool calls; a tool-call bound of zero allows none at all. Every step is written to the order's record as it happens, spend runs against visible credits under a daily cap, and an accepted order is not treated as proof that the work was done: you read its state, progress and attachments.

What to verify before you standardize

• Both tools ran the same real tasks from the same commit with equivalent instructions.
• Acceptance criteria were written before the first run.
• Your written permission policy can be configured in each tool and shared through the repository.
• No tool took an action on your deny list without asking.
• You know the billing route, the behaviour at a limit and the cost per completed task.
• Reviewer time was measured, not guessed.
• Transcripts can be kept alongside the changes they produced.
• The trial ran more than once, and a colleague graded the diffs blind.