The Vibecoding Leaderboard is a new way to benchmark how well AI models actually code — not just talk about code. It evaluates real-world performance across a wide range of technologies using a rigorous, multi-step process.
Each AI model is tested using two autonomous agents:
-
Querier Agent
Prompts the AI to generate code, compiles it, runs it in a sandboxed Docker environment, and submits the output. -
Quantifier Agent
Independently validates the results using logs, outputs, tests, and other tooling. It ensures no model is judged by its own hallucinated standard.
Each AI is scored across multiple objective dimensions:
| Category | What It Measures |
|---|---|
| ✅ Compilation | Does the code build and run successfully? |
| 🧠 Code Quality | Is the code idiomatic, minimal, and correct? |
| 🔐 Security Awareness | Does it handle secrets, roles, and permissions properly? |
| 🧰 Helpfulness | Is the response contextual, version-aware, and good at diagnosing issues? |
Only answers that compile, pass all validations, and demonstrate good practice receive top scores (9–10).
Most AI coding benchmarks rely on static comparisons or hallucinated metrics. Vibecoding simulates real developer workflows — run the code, test the output, and judge only with objective evidence.
No more hand-wavy "correctness" claims. This leaderboard tracks whether AI can vibe like a real engineer.
Learn more at relens.ai