FACTS Benchmark Suite
Models are scored on four factuality tests: Parametric, Search, Multimodal and Grounding v2. The FACTS Score averages accuracy over public and private sets of all four.
Launched 2025-12-09 with 15 models. Kaggle holds the private sets. The Search part became FACTS Search V2 in mid 2026, so later suite averages may not compare.
In the authors’ words
We introduce The FACTS Leaderboard, an online leaderboard suite and associated set of benchmarks that comprehensively evaluates the ability of language models to generate factually accurate text across diverse scenarios. The suite provides a holistic measure of factuality by aggregating the performance of models on four distinct sub-leaderboards: (1) FACTS Multimodal, which measures the factuality of responses to image-based questions; (2) FACTS Parametric, which assesses models' world knowledge by answering closed-book factoid questions from internal parameters; (3) FACTS Search, which evaluates factuality in information-seeking scenarios, where the model must use a search API; and (4) FACTS Grounding (v2), which evaluates whether long-form responses are grounded in provided documents, featuring significantly improved judge models. Each sub-leaderboard employs automated judge models to score model responses, and the final suite score is an average of the four components, designed to provide a robust and balanced assessment of a model's overall factuality. The FACTS Leaderboard Suite will be actively maintained, containing both public and private splits to allow for external participation while guarding its integrity. It can be found at https://www.kaggle.com/benchmarks/google/facts .
- Level
- University Undergraduates and entry-level professionals
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Open-ended Free-form text, documents, designs or media judged as a whole.
GPT-6 Astra leads with 75.4%
Best score per model from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best.
- 75.4 GPT-6 Astra
- 74.6 GPT-6.1 Sol
- 68.5
Gemini 3.8 Flash
- 68.4 GPT-6 Sol
- 65.4
Gemini 3.7 Flash
- 64.9
Gemini 3 Flash
- 64.0 Claude Opus 5.5
- 63.1
Gemini 3.6 Flash
- 62.3 GPT-6 Luna
- 60.6
Gemini 3.1 Pro
- 59.7 Claude Opus 4.8
- 56.5 GPT-5.6 Sol
- 56.2
Gemma 4 31B IT
- 55.3 GPT-5.6 Luna
- 54.0
Gemma 4 26B A4B
- 52.9
Gemini 3.5 Flash
- 50.0 GPT-5.6 Terra
- 47.4
Gemini 3.5 Flash-Lite
- 46.9
Gemini 2.5 Pro
- 45.8 GPT-5.5
- 44.1 GPT-5
- 41.8 GPT-5.1
- 41.5 GPT-5 mini
- 41.0
Gemini 2.5 Flash
- 40.6 Grok 4.5
- 40.1 GPT-4.1
- 39.9 o3
- 39.7 GPT-5.2
- 39.4
Gemini 3.1 Flash-Lite
- 39.2 Claude Opus 4.5
- 36.8 Claude Opus 4.6
- 35.8 Claude Opus 4.7
- 34.5 o4-mini
- 33.1 Claude Opus 4.1
- 32.5 Claude Sonnet 4.6
- 31.1 Claude Sonnet 4.5
- 28.6 Claude Sonnet 4
- 25.7 Grok 4
- 24.6
Gemma 3 27B
- 17.7 Grok 4 Fast
- 1 GPT-6 Astra 75.4%
- 2 GPT-6.1 Sol 74.6%
- 3
Gemini 3.8 Flash 68.5%
- 4 GPT-6 Sol 68.4%
- 5
Gemini 3.7 Flash 65.4%
- 6
Gemini 3 Flash 64.9%
- 7 Claude Opus 5.5 64%
- 8
Gemini 3.6 Flash 63.1%
- 9 GPT-6 Luna 62.3%
- 10
Gemini 3.1 Pro 60.6%
- 11 Claude Opus 4.8 59.7%
- 12 GPT-5.6 Sol 56.5%
- 13
Gemma 4 31B IT 56.2%
- 14 GPT-5.6 Luna 55.3%
- 15
Gemma 4 26B A4B 54%
- 16
Gemini 3.5 Flash 52.9%
- 17 GPT-5.6 Terra 50%
- 18
Gemini 3.5 Flash-Lite 47.4%
- 19
Gemini 2.5 Pro Jun 2025 46.9%
- 20 GPT-5.5 45.8%
- 21 GPT-5 44.1%
- 22 GPT-5.1 41.8%
- 23 GPT-5 mini 41.5%
- 24
Gemini 2.5 Flash Sep 2025 41%
- 25 Grok 4.5 40.6%
- 26 GPT-4.1 40.1%
- 27 o3 39.9%
- 28 GPT-5.2 39.7%
- 29
Gemini 3.1 Flash-Lite 39.4%
- 30 Claude Opus 4.5 39.2%
- 31 Claude Opus 4.6 36.8%
- 32 Claude Opus 4.7 35.8%
- 33 o4-mini 34.5%
- 34 Claude Opus 4.1 33.1%
- 35 Claude Sonnet 4.6 32.5%
- 36 Claude Sonnet 4.5 thinking 31.1%
- 37 Claude Sonnet 4 28.6%
- 38 Grok 4 25.7%
- 39
Gemma 3 27B 24.6%
- 40 Grok 4 Fast reasoning 17.7%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-6 Astra | 75.4% | Sep 2026 | Official leaderboard | |
| 2 | GPT-6.1 Sol | 74.6% | Sep 2026 | Official leaderboard | |
| 3 | | 68.5% | Sep 2026 | Official leaderboard | |
| 4 | GPT-6 Sol | 68.4% | Sep 2026 | Official leaderboard | |
| 5 | | 65.4% | Aug 2026 | Official leaderboard | |
| 6 | | 64.9% | Dec 2025 | Official leaderboard | |
| 7 | Claude Opus 5.5 | 64% | Sep 2026 | Official leaderboard | |
| 8 | | 63.1% | Jul 2026 | Official leaderboard | |
| 9 | GPT-6 Luna | 62.3% | Sep 2026 | Official leaderboard | |
| 10 | | 60.6% | Feb 2026 | Official leaderboard | |
| 11 | Claude Opus 4.8 | 59.7% | May 2026 | Official leaderboard | |
| 12 | GPT-5.6 Sol | 56.5% | Jul 2026 | Official leaderboard | |
| 13 | | 56.2% | Apr 2026 | Official leaderboard | |
| 14 | GPT-5.6 Luna | 55.3% | Jul 2026 | Official leaderboard | |
| 15 | | 54% | Apr 2026 | Official leaderboard | |
| 16 | | 52.9% | May 2026 | Official leaderboard | |
| 17 | GPT-5.6 Terra | 50% | Jul 2026 | Official leaderboard | |
| 18 | | 47.4% | Jul 2026 | Official leaderboard | |
| 19 | | 46.9% | Jun 2025 | Official leaderboard | |
| 20 | GPT-5.5 | 45.8% | Apr 2026 | Official leaderboard | |
| 21 | GPT-5 | 44.1% | Aug 2025 | Official leaderboard | |
| 22 | GPT-5.1 | 41.8% | Nov 2025 | Official leaderboard | |
| 23 | GPT-5 mini | 41.5% | Aug 2025 | Official leaderboard | |
| 24 | | 41% | Sep 2025 | Official leaderboard | |
| 25 | Grok 4.5 | 40.6% | Jul 2026 | Official leaderboard | |
| 26 | GPT-4.1 | 40.1% | Apr 2025 | Official leaderboard | |
| 27 | o3 | 39.9% | Apr 2025 | Official leaderboard | |
| 28 | GPT-5.2 | 39.7% | Dec 2025 | Official leaderboard | |
| 29 | | 39.4% | Mar 2026 | Official leaderboard | |
| 30 | Claude Opus 4.5 | 39.2% | Nov 2025 | Official leaderboard | |
| 31 | Claude Opus 4.6 | 36.8% | Feb 2026 | Official leaderboard | |
| 32 | Claude Opus 4.7 | 35.8% | Apr 2026 | Official leaderboard | |
| 33 | o4-mini | 34.5% | Apr 2025 | Official leaderboard | |
| 34 | Claude Opus 4.1 | 33.1% | Aug 2025 | Official leaderboard | |
| 35 | Claude Sonnet 4.6 | 32.5% | Feb 2026 | Official leaderboard | |
| 36 | Claude Sonnet 4.5 | 31.1% | thinking | Sep 2025 | Official leaderboard |
| 37 | Claude Sonnet 4 | 28.6% | May 2025 | Official leaderboard | |
| 38 | Grok 4 | 25.7% | Jul 2025 | Official leaderboard | |
| 39 | | 24.6% | Mar 2025 | Official leaderboard | |
| 40 | Grok 4 Fast | 17.7% | reasoning | Sep 2025 | Official leaderboard |
Labs report scores for 3 models in their cards. We plot every source in the charts below.
5 models have held number one since its release in Dec 2025
Lab claims count from the day the card came out, independent runs from the model’s release. We draw one row each for the benchmark’s own leaderboard and what labs claim in their cards.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Labs headline it less often now, last in Mar 2026
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 3 Mar 2026 | 40.6 | FACTS Benchmark Suite | |
| 17 Dec 2025 | 61.9 | FACTS Benchmark Suite | |
| 18 Nov 2025 | 70.5 | FACTS Benchmark Suite |
One system card reports it
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 3 Mar 2026 | | 40.6 | FACTS Benchmark Suite (results table, p.4) | Hallucination |
More in Factuality
- SimpleVQA In use Models answer 2,025 short factual questions about images across 9 tasks and 9 topics. An LLM judge grades answers against fixed references.
Qwen3.7-Plus 81.7%
- HallusionBench In use Models answer 1,129 yes or no questions about 346 images, built in pairs to expose language hallucination and visual illusion. Scored by accuracy.
Keye-VL-2.0-30B-A3B 73.5%
- SimpleQA Verified In use 1,000 human-verified, de-duplicated short-answer factual questions measuring parametric knowledge.
Gemini 3.1 Pro 77.5%
- GaRAGe In use Models write long-form RAG answers to 2,366 questions with human-annotated grounding passages. Scoring checks whether answers use the relevant passages.
Nemotron 3.5 Bolt E4B 40.7%
- Kimi Hallu Bench In use Models answer hard prompts drawn from practical work tasks. Fact-checking agents score the share of claims that are hallucinated. Kimi K3.1 Pro 8.30%
- RAGTruth In use A word-level annotated corpus of hallucinations in retrieval-augmented generation answers. It is used to train and evaluate hallucination detection and RAG faithfulness.
Nemotron 3.5 Bolt E4B 77.9%