INDEPENDENT AI MODEL BENCHMARK

QBench Index 1.6

Compare model capability across source-audited evaluations. Explore rankings, benchmark-level evidence, exact effort variants and API price references.

SCORING FRAMEWORK
08 capability families
Equal-weight observed family means
Exact reported effort variants
Unreported scores remain missing

QBench Index 1.6 leaderboard

How QBench Index 1.6 works
Each benchmark keeps its reported 0–100 accuracy or task-success percentage. Results are averaged inside eight capability families, then the observed family means are averaged equally. Missing families are left out, not imputed and not pulled toward 50. Model effort variants are separate entries; unreported effort is never guessed.
Rank: at least 3 eligible evaluations across 3+ capability families · Evidence: full (all 8 families), broad (4–7), or limited (3) · Insufficient evidence: retained in Models, without an Index score.
Pareto frontier: open “Index vs. API price + Pareto”. The dashed frontier is on by default; the API token-price proxy is not cost per task.

Model details