"Which AI tool is best for building dApps?" is unanswerable without a method. This arena teaches the method: fixed tasks, constant prompts, repeated runs, weighted criteria. Contenders are generic Tool A / Tool B / Tool C — all scores are illustrative, not real product measurements.
| Tool | Eff | Cpx | Cost | Spd | Weighted |
|---|
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.
The saved arena has three generic tools and four assigned criteria. Default weights are thirty, twenty five, twenty and twenty five, divided by their sum one hundred. Assigned totals are seventy one point one for A, sixty nine point four for B and seventy four point two for C. At three pixels per score point A measures two hundred thirteen point three, B two hundred eight point two and C two hundred twenty two point six. C wins only under these assumed profiles and preferences. Changing complexity weight to one hundred and all other weights to zero would select B with its stored eighty six complexity score. Generic names and score arrays are illustrative data, not independently tested coding tools or current model performance. The sliders display each raw value with a percent sign, but their sum need not equal one hundred. The algorithm normalizes by their actual total. If all four are one hundred, total four hundred is divided out and each contributes twenty five percent. At point seven five pixels per raw weight point one input one hundred measures seventy five, four-input sum four hundred three hundred and excess over one hundred three hundred two hundred twenty five. If every weight is zero the denominator is replaced by one while every numerator stays zero, making all weighted scores zero. Winner selection keeps A on ties because only a strictly higher score replaces it. The run button merely adds random independent offsets between minus nine and plus nine to twelve stored scores, clamped to one through ninety nine; no benchmark task is executed. Target pillar height is point eight plus seven point five times score divided by one hundred. Default A gives six point one three two five units, B six point zero zero five and C six point three six five. At forty pixels per height unit A rises two hundred forty five point three pixels, B two hundred forty point two and C two hundred fifty four point six from a common baseline. The intended animation moves six percent of the remaining height difference each frame, so the visible crown can lag a new weighted winner. These diagram pillars show the saved formula, not actual WebGL playback. The original local page fails at missing THREE before sliders and run handlers register. Preserve its blank canvas and inert score table; no actual inference, tests, cost measurements, model calls or competitive benchmark is proved.