reasoning benchmark
MASK is a collection of 1000 questions measuring whether models faithfully report their beliefs when pressured to lie.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelXA | Score51.00% | 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 mask 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
Grok-4.1 Thinking currently leads MASK with 51.00%. 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 MASK measures and how its scores work.
MASK is a reasoning benchmark consisting of 1000 questions about whether models report their beliefs faithfully under pressure to lie.
MASK measures the rate at which a model lies, defined as knowingly making false statements intended to be received as true; lower dishonesty rates indicate better honesty.
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 MASK.
Grok-4.1 Thinking is currently ranked first with 51.00%.
The current leaders are Grok-4.1 Thinking (51.00%).
No matched official input price is currently available.
The fastest matched records are Grok-4.1 Thinking (80.00 tok/s via xAI).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
MASK measures the rate at which a model lies, defined as knowingly making false statements intended to be received as true; lower dishonesty rates indicate better honesty.
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.