TruthfulQA
TruthfulQA measures whether language models generate truthful answers to questions. It includes 817 questions spanning 38 categories where humans might give false answers due to misconceptions, superstitions, or conspiracy theories.
Metrics
Truthfulness (%) on 817 questions
Created By
Stephanie Lin et al.
Paper
View paper →Website
Visit website →Top Model Scores
| Rank | Model | Score | Date |
|---|---|---|---|
| 1 | Claude Opus 4.6 | 82.4% | 2026-02 |
| 2 | GPT-5.2 | 80.1% | 2026-03 |
| 3 | Gemini 3 Ultra | 78.6% | 2026-01 |
| 4 | Grok 4 | 76.3% | 2026-02 |
| 5 | Llama 4 405B | 74.9% | 2026-01 |
Related Safety Benchmarks
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SafetyBench evaluates the safety of large language models across 7 categories: offensiveness, unfairness and bias, physical health, mental health, illegal activities, ethics and morality, and privacy. It includes questions in both English and Chinese.
Top: Claude Opus 4.6 — 91.7%
ToxiGen
ToxiGen evaluates the propensity of language models to generate toxic content targeting 13 minority groups. It uses adversarially designed prompts to test whether models produce harmful implicit or explicit toxicity.
Top: Claude Opus 4.6 — 1.2%