legal benchmark
TruthfulQA is a benchmark to measure whether language models are truthful in generating answers to questions. It comprises 817 questions that span 38 categories, including health, law, finance and politics. The questions are crafted such that some humans would answer falsely due to a false belief or misconception, testing models' ability to avoid generating false answers learned from human texts.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | MI | 88.0% | 100.0% | 18 | C | |
| 02 | MI | 77.5% | 94.1% | 18 | C | |
| 03 | IB | 66.9% | 88.2% | 18 | C | |
| 04 | MI | 66.4% | 82.3% | 18 | C | |
| 05 | MI | 64.0% | 76.5% | 18 | C | |
| 06 | NR | 63.3% | 70.6% | 18 | C | |
| 07 | NV | 58.6% | 64.7% | 18 | C | |
| 08 | AC | 58.4% | 58.8% | 18 | C | |
| 09 | AL | 58.3% | 52.9% | 18 | C | |
| 10 | IB | 58.1% | 47.1% | 18 | C | |
| 11 | AC | 57.8% | 41.2% | 18 | C | |
| 12 | CO | 56.3% | 35.3% | 18 | C | |
| 13 | AC | 54.8% | 29.4% | 18 | C | |
| 14 | AC | 54.2% | 23.5% | 18 | C | |
| 15 | AL | 54.1% | 17.6% | 18 | C | |
| 16 | IB | 52.1% | 11.8% | 18 | C | |
| 17 | AC | 50.6% | 5.9% | 18 | C | |
| 18 | MA | 50.3% | 0.0% | 18 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What TruthfulQA measures and how its scores work.
TruthfulQA is a benchmark to measure whether language models are truthful in generating answers to questions. It comprises 817 questions that span 38 categories, including health, law, finance and politics. The questions are crafted such that some humans would answer falsely due to a false belief or misconception, testing models' ability to avoid generating false answers learned from human texts.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about TruthfulQA.
MAI-Thinking-1 is currently ranked first with 88.0%.
TruthfulQA is a benchmark to measure whether language models are truthful in generating answers to questions. It comprises 817 questions that span 38 categories, including health, law, finance and politics. The questions are crafted such that some humans would answer falsely due to a false belief or misconception, testing models' ability to avoid generating false answers learned from human texts.
Yes. Higher values rank better for this benchmark.
18 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.