Developer Builds Repeatable Test Harness to Objectively Compare Free Coding AI Models
A software developer found that standard demo prompts gave misleadingly positive impressions of AI coding models, which often failed on real-world tasks. To address this, he built a lightweight Python harness that runs a fixed set of realistic tasks against multiple models and logs the results for comparison. The task suite includes constraint-following, style-matching, bug localization, and a deliberate impossible prompt designed to expose hallucination under ambiguity. Keeping both the tasks and prompt templates frozen ensures that only the model variable changes between evaluations, making comparisons meaningful. The approach is designed to help developers decide whether a free-tier model is genuinely suitable for their specific workflow before integrating it.
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