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Show HN: Parsimony – an open-source benchmark for how much code AI models add
TL;DR
Parsimony is an open-source benchmark that evaluates how much code AI models add to a codebase, focusing on the net increase in coding units. By measuring added versus removed code, it aims to promote cleaner, more maintainable code in software development.
✦ Why It Matters
Evaluate AI models using Parsimony to ensure they contribute to cleaner, more maintainable codebases.
Key Takeaways
- Use Parsimony to benchmark AI models for code maintainability.
- Focus on net coding units added, not just solve rates.
- Consider the impact of excessive code on long-term projects.
- Evaluate models like GPT-5.6 Sol for their code contribution.
Full Summary
Existing benchmarks for AI models typically assess their ability to solve tasks and the cost of solutions, but they often overlook the impact of excessive code on long-term maintainability. Parsimony addresses this gap by quantifying the net coding units added by AI models, which includes syntax elements like statements and calls while penalizing for deleted code.
The benchmark ranks models based on their mean net units added across tasks, including failed attempts. For instance, the GPT-5.6 Sol model was found to add 0.88 times the code compared to its published performance.
Additionally, the Artificial Analysis Intelligence Index provides an external score for models without affecting their rank. This approach encourages developers to consider not just the effectiveness of AI-generated code but also its maintainability.