VaadinBench tests AI coding agents on real Vaadin tasks
General coding benchmarks tell you how well a model writes code in the abstract. They do not tell you whether an agent can wire a Vaadin Binder correctly, keep a Grid lazily loaded, or reach for a Card component instead of a raw HTML div when that is what the spec calls for.
That gap is why Vaadin built VaadinBench, an open source benchmark designed specifically to measure how AI coding agents perform on real Vaadin development tasks.
How the Benchmark Works
Every VaadinBench task mirrors an actual developer job. Each task includes a written spec, a Vaadin project to work in, and a grading script the agent never sees. The agent writes code; the hidden script scores the result against the same kind of checks a pull request would need to pass.
That last detail matters. The agent cannot reverse engineer the rubric. It has to produce working code, not code that looks plausible.
What It Tests
The benchmark targets Vaadin specific correctness: component usage, data binding, lazy loading behavior, and whether the agent defaults to generic HTML when a purpose built Vaadin component exists. These are the failure modes that cost Java teams real debugging time when they trust an agent without verifying the output.
Models in scope include Claude (including Opus and Haiku variants), OpenAI models, DeepSeek, Llama, and Qwen. The benchmark infrastructure uses Maven, JUnit, and Docker.
Why This Matters for Java Teams
If your team uses Vaadin and you are evaluating which AI coding tool to put in front of your developers, leaderboard scores on HumanEval or SWE-bench do not answer the question. VaadinBench does. It is the closest thing available to a controlled trial of agent performance on your actual stack.
The benchmark is open source, so teams can inspect the tasks, run it locally, and add their own test cases.
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