Kaggle introduces Community Benchmarks, a platform that lets you and the global community design, run, and share custom benchmarks to evaluate artificial intelligence models.
Why does it matter? Because today static metrics aren't enough: models act as agents that reason, generate code, use tools, and handle multiple modalities. Don’t you want evaluations that are dynamic, reproducible, and tied to real-world use cases?
What are Community Benchmarks and why they change the game
Community Benchmarks let you build specific tasks and group them into benchmarks that run against multiple models to produce a reproducible leaderboard.
Instead of a single accuracy score on a fixed dataset, here you can evaluate multi-step reasoning, code generation, tool use, multimodal inputs, and multi-turn conversations. What do you get? An evaluation framework that better reflects how models behave in production scenarios.
