FACTS Grounding
A benchmark evaluating language models' ability to generate factually accurate and well-grounded responses based on long-form input context, comprising 1,719 examples with documents up to 32k tokens requiring detailed responses that are fully grounded in provided documents
Gemini 2.5 Pro Preview 06-05 from Google currently leads the FACTS Grounding leaderboard with a score of 0.878 across 13 evaluated AI models.
Gemini 2.5 Pro Preview 06-05 leads with 87.8%, followed by
Gemini 2.5 Flash at 85.3% and
Gemini 2.5 Flash-Lite at 84.1%.
Progress Over Time
Interactive timeline showing model performance evolution on FACTS Grounding
FACTS Grounding Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | — | — | — | |||
| 2 | Google | — | 1.0M | $0.30 / $2.50 | ||
| 3 | Google | — | — | — | ||
| 4 | Google | — | — | — | ||
| 4 | Google | — | — | — | ||
| 6 | Google | 12B | — | — | ||
| 7 | Google | 27B | — | — | ||
| 8 | Google | — | — | — | ||
| 9 | Google | 4B | — | — | ||
| 10 | Google | — | 1.0M | $0.50 / $3.00 | ||
| 11 | Zhipu AI | — | 200K | $1.20 / $4.00 | ||
| 12 | Google | — | 1.0M | $0.25 / $1.50 | ||
| 13 | Google | 1B | — | — |
FAQ
Common questions about FACTS Grounding.
More evaluations to explore
Related benchmarks in the same category
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.
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.
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.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
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.
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