Zero-Budget Framework for Benchmarking AI Coding Models on Side Projects

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A developer has published a structured, repeatable workflow for evaluating AI coding assistants without spending money, using free-tier APIs and a disposable git repository. The method replaces the common "vibes-based" approach — testing a model with one prompt and judging it on that alone — with a reusable benchmark suite of five to eight task types. A minimal Python harness captures raw model responses, timestamps, and errors for offline comparison across multiple models and runs. The workflow is designed to be provider-agnostic, removing barriers like API costs and the need for local hardware capable of running large models. By versioning prompts and never tuning them to favour a specific model, the approach aims to surface genuine strengths and weaknesses before any real-world deployment.

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