Listen to article 4:02 minutes Today, Kaggle is launching Community Benchmarks, which lets the global AI community design, run and share their own custom benchmarks for evaluating AI models. This is the next step after we launched Kaggle Benchmarks last year, to provide trustworthy and transparent access to evaluations from top-tier research groups like Meta’s MultiLoKo and Google’s FACTS suite. Why community-driven evaluation matters AI capabilities have evolved so rapidly that it’s become difficult to evaluate model performance. Not long ago, a single accuracy score on a static dataset was enough to determine model quality. But today, as LLMs evolve into reasoning agents that collaborate, write code and use tools, those static metrics and simple evaluations are no longer sufficient. Kaggle Community Benchmarks provide developers with a transparent way to validate their specific use cases and bridge the gap between experimental code and production-ready applications. These real-

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