Stay connected with us on X, Discord, and LinkedIn to stay up to date with future analysis
Loading...
All evaluations

AA-Omniscience: Knowledge and Hallucination Benchmark

A benchmark measuring factual recall and hallucination across various economically relevant domains.

Background

AA-Omniscience is a knowledge and hallucination benchmark that rewards accuracy, punishes bad guesses and provides a comprehensive view of which models produce factually reliable outputs across different domains.
The benchmark contains 6,000 questions across 6 major domains, derived from authoritative academic and industry sources and generated automatically using an LLM-based question generation agent to ensure unambiguity, scalability and factual precision

Methodology

All evaluations are conducted independently by Artificial Analysis. More information can be found on our Intelligence Benchmarking Methodology page.

Publication

View on arXiv

AA-Omniscience: Evaluating Cross-Domain Knowledge Reliability in Large Language Models

Declan Jackson, William Keating, George Cameron, Micah Hill-Smith.

Related links

2511.130292511.13029ArtificialAnalysis/AA-Omniscience-PublicArtificialAnalysis/AA-Omniscience-Public

Highlights

  • Gemini 3.1 Pro Preview scores the highest on AA-Omniscience with a score of 33, followed by Gemini 3 Pro Preview (high) with a score of 16, and Claude Opus 4.6 (Adaptive Reasoning, Max Effort) with a score of 14
  • Gemini 3 Pro Preview (high) scores the highest on AA-Omniscience Accuracy with a score of 56%, followed by Gemini 3.1 Pro Preview with a score of 55%, and Gemini 3 Flash Preview (Reasoning) with a score of 54%
  • Grok 4.20 Beta 0309 (Reasoning) scores the lowest on AA-Omniscience Hallucination Rate with a score of 22%, followed by Claude 4.5 Haiku (Non-reasoning) with a score of 25%, and Grok 3 mini Reasoning (high) with a score of 25%

AA-Omniscience Index: Results

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.
+ Add model from specific provider
Independently benchmarked by Artificial Analysis

AA-Omniscience Index

AA-Omniscience Index vs. Artificial Analysis Intelligence Index

AA-Omniscience Index; Artificial Analysis Intelligence Index
+ Add model from specific provider
Most attractive quadrant
Alibaba
Amazon
Anthropic
DeepSeek
Google
Kimi
Korea Telecom
LG AI Research
MBZUAI Institute of Foundation Models
Meta
MiniMax
Mistral
NVIDIA
OpenAI
xAI
Xiaomi
Z AI

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

AA-Omniscience Accuracy

AA-Omniscience Accuracy

AA-Omniscience Accuracy (higher is better) measures the proportion of correctly answered questions out of all questions, regardless of whether the model chooses to answer
+ Add model from specific provider
Reasoning models are indicated by a lightbulb icon.

AA-Omniscience Accuracy (higher is better) measures the proportion of correctly answered questions out of all questions, regardless of whether the model chooses to answer

AA-Omniscience Hallucination Rate

AA-Omniscience Hallucination Rate

AA-Omniscience Hallucination Rate (lower is better) measures how often the model answers incorrectly when it should have refused or admitted to not knowing the answer. It is defined as the proportion of incorrect answers out of all non-correct responses, i.e. incorrect / (incorrect + partial answers + not attempted).
+ Add model from specific provider
Reasoning models are indicated by a lightbulb icon.

AA-Omniscience Hallucination Rate (lower is better) measures how often the model answers incorrectly when it should have refused or admitted to not knowing the answer. It is defined as the proportion of incorrect answers out of all non-correct responses, i.e. incorrect / (incorrect + partial answers + not attempted).

Detailed Domain Results

AA-Omniscience Index Across Domains (Normalized)

AA-Omniscience Index; Scores are normalized per domain across all models tested, where green represents the highest score for that domain and red represents the lowest score for that domain.
+ Add model from specific provider
Reasoning model

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Software Engineering Deep Dive

Software Engineering AA-Omniscience Index Across Languages (Normalized)

Software Engineering AA-Omniscience Index; Scores are normalized per language across all models tested, where green represents the highest score for that language and red represents the lowest score for that language.
+ Add model from specific provider
Reasoning model

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

AA-Omniscience Index Question Breakdown

Distribution of questions across different domains and subdomains in the AA-Omniscience benchmark

Business

Humanities & Social Sciences

Science, Engineering & Mathematics

Health

Law

Software Engineering (SWE)

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Model Size (Open Weights Models Only)

AA-Omniscience Index vs. Total Parameters

AA-Omniscience Index; Size in Parameters (Billions)
+ Add model from specific provider
Most attractive quadrant
Alibaba
DeepSeek
Kimi
Korea Telecom
LG AI Research
MBZUAI Institute of Foundation Models
Meta
Mistral
NVIDIA
OpenAI
Xiaomi
Z AI

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

AA-Omniscience Accuracy vs. Total Parameters

AA-Omniscience Accuracy; Size in Parameters (Billions)
+ Add model from specific provider
Most attractive quadrant
Alibaba
DeepSeek
Kimi
Korea Telecom
LG AI Research
MBZUAI Institute of Foundation Models
Meta
Mistral
NVIDIA
OpenAI
Xiaomi
Z AI

AA-Omniscience Accuracy (higher is better) measures the proportion of correctly answered questions out of all questions, regardless of whether the model chooses to answer

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

AA-Omniscience Hallucination Rate vs. Total Parameters

AA-Omniscience Hallucination Rate; Size in Parameters (Billions)
+ Add model from specific provider
Most attractive quadrant
Alibaba
DeepSeek
Kimi
Korea Telecom
LG AI Research
MBZUAI Institute of Foundation Models
Meta
Mistral
NVIDIA
OpenAI
Xiaomi
Z AI

AA-Omniscience Hallucination Rate (lower is better) measures how often the model answers incorrectly when it should have refused or admitted to not knowing the answer. It is defined as the proportion of incorrect answers out of all non-correct responses, i.e. incorrect / (incorrect + partial answers + not attempted).

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

AA-Omniscience Index: Token Usage

+ Add model from specific provider
Tokens used to run the evaluation
Input tokens
Reasoning tokens
Answer tokens

The total number of tokens used to run the evaluation, including input tokens (prompt), reasoning tokens (for reasoning models), and answer tokens (final response).

AA-Omniscience Index: Cost Breakdown

+ Add model from specific provider
Cost (USD) to run the evaluation
Input cost
Reasoning cost
Answer cost

The cost to run the evaluation, calculated using the model's input and output token pricing and the number of tokens used.

AA-Omniscience Index: Score vs. Release Date

+ Add model from specific provider
Most attractive region
Alibaba
Amazon
Anthropic
DeepSeek
Google
Kimi
Korea Telecom
LG AI Research
MBZUAI Institute of Foundation Models
Meta
MiniMax
Mistral
NVIDIA
OpenAI
xAI
Xiaomi
Z AI

Example Problems

Explore Evaluations

Artificial Analysis Intelligence IndexArtificial Analysis Intelligence Index

A composite benchmark aggregating ten challenging evaluations to provide a holistic measure of AI capabilities across mathematics, science, coding, and reasoning.

GDPval-AA LeaderboardGDPval-AA Leaderboard

GDPval-AA is Artificial Analysis' evaluation framework for OpenAI's GDPval dataset. It tests AI models on real-world tasks across 44 occupations and 9 major industries. Models are given shell access and web browsing capabilities in an agentic loop via Stirrup to solve tasks, with ELO ratings derived from blind pairwise comparisons.

𝜏²-Bench Telecom Benchmark Leaderboard𝜏²-Bench Telecom Benchmark Leaderboard

A dual-control conversational AI benchmark simulating technical support scenarios where both agent and user must coordinate actions to resolve telecom service issues.

Terminal-Bench Hard Benchmark LeaderboardTerminal-Bench Hard Benchmark Leaderboard

An agentic benchmark evaluating AI capabilities in terminal environments through software engineering, system administration, and data processing tasks.

SciCode Benchmark LeaderboardSciCode Benchmark Leaderboard

A scientist-curated coding benchmark featuring 338 sub-tasks derived from 80 genuine laboratory problems across 16 scientific disciplines.

Artificial Analysis Long Context Reasoning Benchmark LeaderboardArtificial Analysis Long Context Reasoning Benchmark Leaderboard

A challenging benchmark measuring language models' ability to extract, reason about, and synthesize information from long-form documents ranging from 10k to 100k tokens (measured using the cl100k_base tokenizer).

AA-Omniscience: Knowledge and Hallucination BenchmarkAA-Omniscience: Knowledge and Hallucination Benchmark

A benchmark measuring factual recall and hallucination across various economically relevant domains.

IFBench Benchmark LeaderboardIFBench Benchmark Leaderboard

A benchmark evaluating precise instruction-following generalization on 58 diverse, verifiable out-of-domain constraints that test models' ability to follow specific output requirements.

Humanity's Last Exam Benchmark LeaderboardHumanity's Last Exam Benchmark Leaderboard

A frontier-level benchmark with 2,500 expert-vetted questions across mathematics, sciences, and humanities, designed to be the final closed-ended academic evaluation.

GPQA Diamond Benchmark Leaderboard

The most challenging 198 questions from GPQA, where PhD experts achieve 65% accuracy but skilled non-experts only reach 34% despite web access.

CritPt Benchmark LeaderboardCritPt Benchmark Leaderboard

A benchmark designed to test LLMs on research-level physics reasoning tasks, featuring 71 composite research challenges.

Artificial Analysis Openness IndexArtificial Analysis Openness Index

A composite measure providing an industry standard to communicate model openness for users and developers.

MMLU-Pro Benchmark LeaderboardMMLU-Pro Benchmark Leaderboard

An enhanced version of MMLU with 12,000 graduate-level questions across 14 subject areas, featuring ten answer options and deeper reasoning requirements.

Global-MMLU-Lite Benchmark LeaderboardGlobal-MMLU-Lite Benchmark Leaderboard

A lightweight, multilingual version of MMLU, designed to evaluate knowledge and reasoning skills across a diverse range of languages and cultural contexts.

LiveCodeBench Benchmark LeaderboardLiveCodeBench Benchmark Leaderboard

A contamination-free coding benchmark that continuously harvests fresh competitive programming problems from LeetCode, AtCoder, and CodeForces, evaluating code generation, self-repair, and execution.

MATH-500 Benchmark LeaderboardMATH-500 Benchmark Leaderboard

A 500-problem subset from the MATH dataset, featuring competition-level mathematics across six domains including algebra, geometry, and number theory.

AIME 2025 Benchmark LeaderboardAIME 2025 Benchmark Leaderboard

All 30 problems from the 2025 American Invitational Mathematics Examination, testing olympiad-level mathematical reasoning with integer answers from 000-999.

MMMU-Pro Benchmark LeaderboardMMMU-Pro Benchmark Leaderboard

An enhanced MMMU benchmark that eliminates shortcuts and guessing strategies to more rigorously test multimodal models across 30 academic disciplines.