New open-source benchmark measures AI code generation efficiency, penalizing bloat

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A developer has created an open-source benchmark called Parsimony to evaluate AI code generation models. It measures net code addition by analyzing published patches from existing benchmarks, counting core syntax units instead of lines of code. The tool subtracts deleted code to assess how much new complexity a model adds to a codebase. The creator notes that newer models like GPT-6 and Claude 5.5 are not yet fully represented as they lack public benchmark data. The project is available on GitHub and seeks community feedback and contributions.

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