SimpleQA Verified

SimpleQA Verified is a curated, reliability-focused subset of SimpleQA that addresses label noise and redundancy in the original benchmark, measuring short-form parametric factual accuracy of large language models on fact-seeking questions with single, indisputable answers.

MAI-Thinking-1 from Microsoft currently leads the SimpleQA Verified leaderboard with a score of 0.310 across 1 evaluated AI models.

Paper
About this benchmark

What SimpleQA Verified measures

SimpleQA Verified is a text benchmark that evaluates large language models on reasoning, factuality, and general tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.3, with the leader reaching 0.3.

Compare leaders on the best AI for reasoning, best AI for factuality and best AI for general leaderboards.

Publication

Paper
SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge
Authors
Lukas Haas, Gal Yona, Giovanni D'Antonio, Sasha Goldshtein, and 1 others
Published

Abstract

We introduce SimpleQA Verified, a 1,000-prompt benchmark for evaluating Large Language Model (LLM) short-form factuality based on OpenAI's SimpleQA. It addresses critical limitations in OpenAI's benchmark, including noisy and incorrect labels, topical biases, and question redundancy. SimpleQA Verified was created through a rigorous multi-stage filtering process involving de-duplication, topic balancing, and source reconciliation to produce a more reliable and challenging evaluation set, alongside improvements in the autorater prompt. On this new benchmark, Gemini 2.5 Pro achieves a state-of-the-art F1-score of 55.6, outperforming other frontier models, including GPT-5. This work provides the research community with a higher-fidelity tool to track genuine progress in parametric model factuality and to mitigate hallucinations. The benchmark dataset, evaluation code, and leaderboard are available at: https://www.kaggle.com/benchmarks/deepmind/simpleqa-verified.

MicrosoftMAI-Thinking-1 leads with 31.0%.

Progress Over Time

Interactive timeline showing model performance evolution on SimpleQA Verified

State-of-the-art frontier
Open
Proprietary

SimpleQA Verified Leaderboard

1 models
ContextCostLicense
11.0T
Notice missing or incorrect data?

FAQ

Common questions about SimpleQA Verified.

What is the SimpleQA Verified benchmark?

SimpleQA Verified is a curated, reliability-focused subset of SimpleQA that addresses label noise and redundancy in the original benchmark, measuring short-form parametric factual accuracy of large language models on fact-seeking questions with single, indisputable answers.

What is the SimpleQA Verified leaderboard?

The SimpleQA Verified leaderboard ranks 1 AI models based on their performance on this benchmark. Currently, MAI-Thinking-1 by Microsoft leads with a score of 0.310. The average score across all models is 0.310.

What is the highest SimpleQA Verified score?

The highest SimpleQA Verified score is 0.310, achieved by MAI-Thinking-1 from Microsoft.

How many models are evaluated on SimpleQA Verified?

1 models have been evaluated on the SimpleQA Verified benchmark, with 0 verified results and 1 self-reported results.

Where can I find the SimpleQA Verified paper?

The SimpleQA Verified paper is available at https://arxiv.org/abs/2509.07968. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does SimpleQA Verified cover?

SimpleQA Verified is categorized under reasoning, factuality, and general. The benchmark evaluates text models.

What's the difference between SimpleQA Verified and SimpleQA?

SimpleQA Verified is a variant of SimpleQA. See the SimpleQA leaderboard for the broader benchmark and per-model comparison.

How recent are the SimpleQA Verified leaderboard results?

The SimpleQA Verified leaderboard was last updated in June 2026 and currently includes 1 evaluated models.

More evaluations to explore

Related benchmarks in the same category

View all reasoning
GPQA

A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.

reasoning
223 models
MMLU-Pro

A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.

reasoning
127 models
AIME 2025

All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.

reasoning
113 models
MMLU

Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains

reasoning
100 models
SWE-Bench Verified

A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.

reasoning
99 models
Humanity's Last Exam

Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions

reasoningmultimodal
82 models