Comparing AI Coding Agents for a Large Existing Repository

A coding assistant that handles a short example may struggle in an existing repository. Before paying for one, test whether it can find the right files, follow the project's conventions and make a change without breaking its neighbors. That's where repository-level help has to earn its value.

Once you know which capabilities the repository needs, compare plan prices, usage limits and model access to narrow the candidates worth testing.

Copying code into chat becomes awkward once a bug crosses several files. The missing context may be a call site, a build option or an assumption in an older module. A useful agent should help locate that context rather than ask you to paste more and more of the project.

Separate discovery from implementation

Before requesting a fix, ask the candidate to identify the entry point, the main data type, and the callers involved in one behavior. Require file references. A confident architecture summary without references is difficult to audit.

Then open two or three of those references yourself. Does the description match the code? Did the assistant find production paths or only a test fixture? Did it notice platform-specific implementations? In a C++ solution, a symbol appearing in a header does not establish which implementation is linked into the executable you run.

This discovery pass answers a more useful question than “Can it read the whole repo?” You want to know whether it can retrieve enough correct context for the task while leaving irrelevant material out.

Give each tool the same acceptance contract

Choose a modest change with a real edge case. For example, change a configuration option from one global value to a value scoped to each document. That requires following ownership, updating callers, and preserving old behavior where the option is absent.

Prepare a brief before either assistant starts:

• Starting commit and supported operating system.
• The behavior to change and the behavior to preserve.
• Files or generated artifacts it must not edit.
• The build and test commands you normally trust.
• An example input and the expected result.

Keep a copy of this brief. Otherwise it is easy to unconsciously give the second tool a better prompt because the first one taught you where the bug lives.

Compare the complete change

Use a small review sheet rather than a general star rating:

If the build cannot run in the tool's environment, mark it unverified. Do not translate “I inspected the code” into “the project builds.” A cloud environment may lack your licensed toolchain, native dependency, or Windows-specific component. A local agent may have the environment but still invoke the wrong configuration.

Keep the results in a short note so you can compare the candidates against the same checks.

Do not confuse client, model and subscription

The same model family can appear through different clients, each with its own context gathering and billing. A familiar model name in a dropdown does not guarantee an identical workflow.

Current Cursor CLI documentation includes terminal workflows. The Claude Code subscription guide includes supported IDEs. A simple “editor versus terminal” comparison therefore misses the overlap. Test the interface you intend to use, with the account and model route you intend to pay for.

Also record whether the tool uses subscription authentication or an API key. That affects which usage meter matters. When a task hits a limit, note the reset window and unfinished work; do not assume the same interruption would occur for every user on the named plan.

Keep context portable

At the end of a session, save a short handoff: goal, relevant files, completed changes, failed checks, and the next command to run. Keep this in your working notes or issue. Avoid copying a huge transcript into every new chat.

For example: “Configuration parsing is updated; two callers remain; debug build passes; release build not run; reproduce with this fixture.” That is enough to resume with another tool or manually, and it does not require sharing every unrelated file.

Choose the tool that makes a correct change you can follow through the repository. A large context window is only useful if the agent finds and uses the right information. Keep the test task and its checks so you can repeat the comparison when the product or your project changes.