Introducing Community Benchmarks on Kaggle

Source: Dev.to

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. Today, as LLMs become reasoning agents that collaborate, write code, and use tools, static metrics and simple evaluations are no longer sufficient.

Kaggle Community Benchmarks give developers a transparent way to validate specific use cases and bridge the gap between experimental code and production‑ready applications. Real‑world use cases demand a more flexible and transparent evaluation framework, and Community Benchmarks provide a dynamic, rigorous, and continuously evolving approach shaped by the users building and deploying these systems every day.

How to build your own benchmarks on Kaggle

Benchmarks start with building tasks, which can range from evaluating multi‑step reasoning and code generation to testing tool use or image recognition. Once you have tasks, you can add them to a benchmark to evaluate and rank selected models across the tasks.

Create a task

Tasks test an AI model’s performance on a specific problem. They allow you to run reproducible tests across different models to compare accuracy and capabilities.

Create a benchmark

After creating one or more tasks, group them into a benchmark. A benchmark lets you run tasks across a suite of leading AI models and generate a leaderboard to track and compare performance.

Benefits

• Broad model access – Free access (within quota limits) to state‑of‑the‑art models from labs like Google, Anthropic, DeepSeek, and more.
• Reproducibility – Benchmarks capture exact outputs and model interactions so results can be audited and verified.
• Complex interactions – Support for multi‑modal inputs, code execution, tool use, and multi‑turn conversations.
• Rapid prototyping – Quickly design and iterate on creative new tasks.

These capabilities are powered by the new kaggle‑benchmarks SDK (GitHub repository).

Resources

• Benchmarks Cookbook – A guide to advanced features and use cases.
• Example tasks – Get inspired with a variety of pre‑built tasks.
• Getting started – How to create your first task & benchmark.

How we’re shaping the future of AI evaluation

The future of AI progress depends on how models are evaluated. With Kaggle Community Benchmarks, Kagglers are no longer just testing models—they’re helping shape the next generation of intelligence.

Ready to build? Try Community Benchmarks today.