legal benchmark
GDPval-Rubrics evaluates AI model performance on economically valuable knowledge work tasks from the public GDPval dataset using public rubrics.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMI | Score74.78% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis gdpval-rubrics AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
MiniMax M3 currently leads GDPval-Rubrics with 74.78%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What GDPval-Rubrics measures and how its scores work.
GDPval-Rubrics is a benchmark based on economically valuable knowledge work tasks drawn from the public GDPval dataset, with an environment aligned to the GDPval-AA scaffolding.
It measures performance using pointwise scoring based on public rubrics, reported as a Score ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about GDPval-Rubrics.
MiniMax M3 is currently ranked first with 74.78%.
The current leaders are MiniMax M3 (74.78%).
MiniMax M3 has the lowest matched official input price at $0.30 input / $1.2 output per 1M tokens.
The fastest matched records are MiniMax M3 (6.61 tok/s via MiniMax).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures performance using pointwise scoring based on public rubrics, reported as a Score ratio.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.