llmboard.aiLeaderboard Center
Overall
Overall RankingOpen Models
Tools
Model DirectoryCompare Models
Capabilities
CodingReasoningMathKnowledgeInstruction Following
Price & Efficiency
Price & ValueCapability vs. PriceRuntime Performance
Modalities
Image GenerationVideo GenerationSpeech ModelsEmbeddings
Core Benchmarks
GPQAMMLU-ProAIME 2025SWE-Bench VerifiedMMLUHumanity's Last ExamLiveCodeBenchMATHHumanEvalMMMU-ProView all benchmarks
Methods
Scoring & Data
393 models668 benchmarks

Leaderboard Center

Overall RankingCodingCore BenchmarksPrice & ValueRuntime Performance

Modalities

All ModelsImage GenerationImage EditingVideo GenerationImage-to-VideoVideo EditingText-to-SpeechSpeech-to-TextEmbeddings

Data & Methods

Scoring MethodAll BenchmarksReasoningMath

Vendors

All VendorsOpenAIAnthropicGoogle
llmboard.aiCopyright 2026 llmboard.ai

legal benchmark

TruthfulQA

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

Models18
Model coverage18
MetricScore
EvidenceB

On this page

  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

TruthfulQA Ranking

Higher score ranks better on this benchmark.

18 rows
Columns

Show columns

01MIMAI-Thinking-1Microsoft88.0%100.0%18CAug 11, 2026
02MIPhi-3.5-MoE-instructMicrosoft77.5%94.1%18CAug 11, 2026
03IBGranite 3.3 8B InstructIBM66.9%88.2%18CAug 11, 2026
04MIPhi 4 MiniMicrosoft66.4%82.3%18CAug 11, 2026
05MIPhi-3.5-mini-instructMicrosoft64.0%76.5%18CAug 11, 2026
06NRHermes 3 70BNous Research63.3%70.6%18CAug 11, 2026
07NVLlama 3.1 Nemotron 70B InstructNVIDIA58.6%64.7%18CAug 11, 2026
08ACQwen2.5 14B InstructAlibaba Cloud / Qwen Team58.4%58.8%18CAug 11, 2026
09ALJamba 1.5 LargeAI21 Labs58.3%52.9%18CAug 11, 2026
10IBIBM Granite 4.0 Tiny PreviewIBM58.1%47.1%18CAug 11, 2026
11ACQwen2.5 32B InstructAlibaba Cloud / Qwen Team57.8%41.2%18CAug 11, 2026
12COCommand R+Cohere56.3%35.3%18CAug 11, 2026
13ACQwen2 72B InstructAlibaba Cloud / Qwen Team54.8%29.4%18CAug 11, 2026
14ACQwen2.5-Coder 32B InstructAlibaba Cloud / Qwen Team54.2%23.5%18CAug 11, 2026
15ALJamba 1.5 MiniAI21 Labs54.1%17.6%18CAug 11, 2026
16IBGranite 3.3 8B BaseIBM52.1%11.8%18CAug 11, 2026
17ACQwen2.5-Coder 7B InstructAlibaba Cloud / Qwen Team50.6%5.9%18CAug 11, 2026
18MAMistral NeMo InstructMistral AI50.3%0.0%18CAug 11, 2026

TruthfulQA Score Distribution

A closer view of the leading scores on this benchmark.

TruthfulQA

TruthfulQA Highlights

The leading models and scores on this benchmark.

Rank #1MAI-Thinking-188.0%Rank #2Phi-3.5-MoE-instruct77.5%Rank #3Granite 3.3 8B Instruct66.9%Rank #4Phi 4 Mini66.4%

What is TruthfulQA?

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.

Family
TruthfulQA
Modality
text
Primary category
legal
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
truthfulqa|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about TruthfulQA.

Which model scores highest on TruthfulQA?

MAI-Thinking-1 is currently ranked first with 88.0%.

What does TruthfulQA measure?

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.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

18 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.