You can waste hours comparing coding agents by reading model cards or watching polished demos. The cheaper signal is a tiny audit that records whether the agent changes only the intended files and leaves the test suite green after one under-specified, realistic task. This guide gives you the harness, the fixture pattern, and the reading rubric.
MonkeyCode is a useful platform for this because its free model access lets you repeat the same probe across different model labels without metering every call, and its free server option removes your laptop's memory and GPU as the hidden variable. Disclosure: This article was prepared as part of MonkeyCode's product outreach.
What you are really comparing
You are not measuring whether the model can solve a clean LeetCode prompt. You are measuring whether it can make a contained change in a disposable Git directory without producing collateral damage. The audit records five signals: the agent command's exit code, elapsed time, modified files, the tail of the agent's stdout and stderr, and the test suite's exit code after the change. Those five signals are boring, and that is the point; they are cheap to collect and hard to argue with.
You should repeat the same fixture against different runtimes. A local run tells you how the agent behaves when it shares your machine's dependencies and network rules. A server-backed run tells you how much of the result depends on a controlled environment rather than your laptop. The comparison is only meaningful if the fixture and the test command stay identical across runs.
