Benchmark Arena: AI Coding Tools

Fair benchmarking checklist

• Fixed task suite: define the tasks first (e.g., 10 dApp tasks: token mint, wallet connect, program tests) — never pick tasks after seeing results.
• Constant prompts: identical instructions and context to every tool; no hand-tuned prompts for a favorite.
• n > 1 runs: LLM output is stochastic. Run each task several times and report mean and spread — the Run button above shows how a single trial can flip the podium.
• pass@k: probability at least one of k samples passes the tests; report pass@1 for honesty, pass@k for potential.
• Count human interventions: every manual fix is hidden cost — log it.
• Verify with tests, not vibes: a task "succeeds" only if an automated test suite passes.

The metric that settles arguments

A tool that costs 3× more per call but solves 4× more tasks is cheaper where it counts. Likewise normalize time: minutes per solved task, including retries and your own debugging time.

Pitfalls that fake a winner

• Cherry-picking: showcasing the one great run out of ten.
• Contamination: the benchmark task (or its solution) was in the model's training data — common with famous tutorials and public repos.
• Task monoculture: only CRUD apps, then claiming general superiority.
• Moving goalposts: changing accept criteria between tools.