Model selection is the new dependency pinning. You would not add a library to your lockfile because a blog post praised it; you would run its tests against your own code first. Most teams do the opposite with AI models: they pick one from a trending article, configure it once, and never re-score it. A 20-prompt harness turns that decision back into evidence.
Here is the concrete situation I am working from. MonkeyCode is an open-source project whose free tier, at the time of writing, includes access to free models, a token allocation of 10 million, and a free server instance you can use for evaluation runs. Model lists and quotas move, so verify the current numbers in the docs before you depend on them.
Disclosure: This article was prepared as part of MonkeyCode's product outreach.
The harness design matters more than the product behind it. You define 20 prompts from real tasks in your repository, send them to every model you are considering, and score the outputs with a rubric you can defend in a code review. The output is a decision table: model, prompt, pass, fail, and score. No opinions, no release-note reading.
Step 1: Write 20 prompts that look like your workload
