When a new low-cost model appears, launch-day benchmarks tend to dominate the discussion. Developers rarely need a global leaderboard; they need to know whether the model breaks a critical parse function, emits an unsafe shell command, or changes behavior on a repo-specific bug fix. That answer cannot come from a vendor chart.
This article presents a small regression harness for comparing a candidate model with a baseline on a handful of private, deterministic tasks. The harness is useful when a provider offers free model access or a free server option; MonkeyCode is used here as one example of such a provider. Disclosure: This article was prepared as part of MonkeyCode's product outreach.
No model names, quotas, or ranking numbers are asserted here. The examples use candidate and baseline because model identifiers change quickly; replace them with exact strings from the provider's documentation.
The approach has three parts: a fixed task suite, a uniform runner, and a pass/fail scorecard. A new model is not “better” in this workflow; it is either safe to try on the next task or not yet ready.
1. Define a Small Task Suite
