Model comparison increasingly relies on large collections of publicly reported benchmark scores, yet common aggregation strategies trade off evidence coverage against control over capability weighting. Manually curated suites leave potentially informative evaluations unused, while uniform averaging retains them but gives greater influence to capabilities that happen to be benchmarked more densely. We introduce Balance of Benchmarks (BoB), a framework that retains eligible benchmark evidence while adapting its influence for task-conditioned model comparison using only public aggregate scores. BoB combines semantic density weighting, score equating across benchmarks of different difficulty, and task-relevant residual pooling. We evaluate it on 605 configurations across 14 Artificial Analysis benchmarks and on WildScores, a collection of 148 developer-reported benchmarks evaluated with held-out source-lineage families. On WildScores, BoB-Support raises family-mean Spearman correlation from 0.764 under uniform standardized averaging to 0.823, reduces MAE from 6.19 to 5.10 normalized score points, and increases three-model shortlist hit rate from 65.3% to 72.6%. BoB-Constant reaches a Spearman correlation of 0.831 and a hit rate of 74.6%. Separately, density weighting reduces average ranking changes when benchmarks are repeated, including as paraphrased copies. BoB-Support also reduces retrospective three-model shortlist regret from 2.08 to 1.67 normalized score points. BoB makes benchmark inclusion, redundancy, and task relevance explicit and testable measurement choices, allowing existing benchmark evidence to be used more fully while moderating the influence of benchmark proliferation.

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