Zero-Budget AI Coding Model Evaluation: A Sandbox-First Workflow
You don't need a paid API or a production risk to find out whether an AI coding model is worth your time. This sandbox-first workflow uses a versioned prompt suite, a throwaway repo, and a free-tier endpoint to turn "does this model feel good?" into a rerunnable benchmark—all on a zero budget. There's a conversation happening right now about what happens when we hand AI agents more tools and the…
This article outlines a zero-budget workflow for evaluating AI coding models before committing any resources. The key steps involve creating a fixed prompt suite, running the prompts through a harness that captures raw responses, and scoring the results to reveal whether the model performs well across different types of tasks. The approach uses a versioned prompt suite in a throwaway Git repository, a free-tier endpoint, and emphasizes repeatability by setting the temperature to zero and avoiding model-specific tuning.
By treating model evaluation as a rerunnable benchmark rather than a first impression, developers can identify models that actually help with their specific workflows without incurring any costs.
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