SimpleQA
BenchmarkFreeOpenAI's factuality benchmark for hallucination detection.
Capabilities5 decomposed
factuality-benchmark-evaluation-with-unambiguous-answers
Medium confidenceEvaluates language model factuality by presenting short, fact-seeking questions with objectively verifiable answers, eliminating ambiguity through careful question curation and answer validation. The benchmark uses a curated dataset of questions where ground-truth answers are unambiguous and verifiable, enabling precise measurement of hallucination rates versus correct factual retrieval. Scoring is binary (correct/incorrect) based on exact or semantically equivalent answer matching against a gold standard answer set.
Focuses specifically on unambiguous factual questions to isolate hallucination measurement from reasoning or interpretation ambiguity; curated dataset design ensures binary correctness judgments without subjective evaluation, enabling precise quantification of factuality gaps across model families
More focused on pure factuality than general knowledge benchmarks like MMLU or TruthfulQA, which mix reasoning and knowledge; eliminates subjective answer evaluation through unambiguous ground truth, providing cleaner signal than human-judged benchmarks
hallucination-rate-quantification-across-model-variants
Medium confidenceProduces quantitative hallucination metrics by running identical questions across multiple model variants and comparing answer correctness rates, enabling direct measurement of how model size, training approach, or architecture affects factual accuracy. The benchmark infrastructure supports batch evaluation of multiple models against the same question set, generating comparative metrics that isolate hallucination as a distinct failure mode from other error types.
Provides standardized hallucination quantification through a fixed benchmark set, enabling reproducible cross-model comparison without subjective evaluation; unambiguous answers allow precise percentage-based hallucination rates rather than fuzzy confidence intervals
More precise hallucination measurement than general accuracy benchmarks because it isolates factual correctness from reasoning ability; enables direct model-to-model comparison on identical questions, unlike ad-hoc evaluation approaches
ground-truth-answer-validation-and-matching
Medium confidenceValidates model-generated answers against a curated set of ground-truth answers using exact string matching, semantic equivalence checking, or normalized comparison (handling variations like spelling, punctuation, or synonyms). The benchmark infrastructure includes answer validation logic that maps model outputs to gold-standard answers, supporting multiple valid answer formats while rejecting plausible but incorrect responses that would pass simple keyword matching.
Uses unambiguous ground-truth answers to enable deterministic validation without subjective judgment; supports multiple valid answer formats while maintaining binary correctness judgments, eliminating the need for human evaluation or fuzzy scoring
More reproducible than human-judged evaluation because scoring is deterministic and auditable; more precise than keyword-matching approaches because it validates semantic correctness rather than surface-level answer presence
factual-knowledge-domain-coverage-assessment
Medium confidenceAssesses which domains and types of factual knowledge a model handles well versus poorly by organizing benchmark questions across implicit or explicit categories (e.g., history, geography, science, current events). The benchmark enables analysis of factuality performance stratified by question type, revealing whether hallucination is uniform across domains or concentrated in specific knowledge areas where models are more prone to confabulation.
Enables domain-stratified factuality analysis by organizing unambiguous questions across implicit knowledge categories, revealing whether hallucination is uniform or concentrated in specific domains where models lack training coverage or struggle with reasoning
More actionable than aggregate hallucination rates because it identifies specific domains where models are unreliable, enabling targeted mitigation (e.g., RAG for weak domains); more focused than general knowledge benchmarks that don't isolate factuality from reasoning
reproducible-model-factuality-regression-testing
Medium confidenceProvides a fixed benchmark set enabling reproducible evaluation of model factuality across time, versions, and configurations, supporting regression testing to detect when model updates degrade factual accuracy. The benchmark infrastructure allows teams to run identical evaluations on different model versions or configurations, generating comparable metrics that reveal whether changes improved or harmed factuality without confounding variables.
Provides a standardized, fixed benchmark enabling reproducible factuality measurement across model versions and time, supporting regression detection without confounding variables; unambiguous answers ensure consistent scoring across evaluation runs
More reproducible than ad-hoc evaluation because the benchmark is fixed and publicly available; enables continuous monitoring unlike one-time evaluation; more focused on factuality regression than general performance benchmarks
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with SimpleQA, ranked by overlap. Discovered automatically through the match graph.
TruthfulQA
817 adversarial questions measuring model truthfulness vs misconceptions.
ragas
Evaluation framework for RAG and LLM applications
TrustLLM
8-dimension trustworthiness benchmark for LLMs.
Athina AI
LLM eval and monitoring with hallucination detection.
gaia
Dataset by siril-spcc. 2,99,750 downloads.
Galileo
AI evaluation platform with hallucination detection and guardrails.
Best For
- ✓AI researchers evaluating model factuality across model families
- ✓LLM product teams assessing hallucination risk before deployment
- ✓Teams building retrieval-augmented generation (RAG) systems needing factuality baselines
- ✓Organizations comparing proprietary vs open-source models on factual accuracy
- ✓Model developers optimizing for factuality during training or fine-tuning
- ✓Teams evaluating whether to upgrade to a newer model version based on factuality improvements
- ✓Researchers studying the relationship between model scale and hallucination propensity
- ✓Product managers making go/no-go decisions for LLM deployment based on factuality thresholds
Known Limitations
- ⚠Limited to short-form factual questions; does not measure reasoning, multi-step inference, or nuanced understanding
- ⚠Unambiguous answer requirement excludes domains with legitimate disagreement or context-dependent correctness
- ⚠No measurement of confidence calibration — models may be confidently wrong or uncertain when correct
- ⚠Benchmark size and composition not disclosed; potential for overfitting if models are fine-tuned on similar data
- ⚠Does not capture temporal degradation of factual knowledge or performance on very recent events
- ⚠Hallucination rate is a single aggregate metric; does not break down by question category, difficulty, or domain
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
OpenAI's factuality benchmark containing short, fact-seeking questions with unambiguous answers, designed to measure how often language models provide correct factual information versus hallucinating plausible responses.
Categories
Alternatives to SimpleQA
Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.
Compare →Amplication brings order to the chaos of large-scale software development by creating Golden Paths for developers - streamlined workflows that drive consistency, enable high-quality code practices, simplify onboarding, and accelerate standardized delivery across teams.
Compare →Are you the builder of SimpleQA?
Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.
Get the weekly brief
New tools, rising stars, and what's actually worth your time. No spam.
Data Sources
Looking for something else?
Search →