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 December 9th, 2025 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.
- Made by
- Google DeepMind + Google Research + Kaggle
- Released
- December 2025
- Kind
- Short factual questions
- Level
- University
- Scored
- Graded by a model
- Answers
- Open-ended
- Tasks
- Public, plus a private test set
- Strongest model tested
-
Gemini 4 Carbon #2 on the capability index
- Last in a launch post
- April 2026, dropped before it saturated
- Last model added
- October 2026
- Scores refreshed
- November 12th, 2026
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 .
Gemini 4 Carbon leads with 78.3%
Best score per model from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best.
Gemini 4 Carbon leads with 78.3%, claims included
Best score per model from every source, including labs’ own numbers and claims Epoch AI collected from reports and papers. Labs pick their own settings, so a claim can beat an independent run of the same model.
- 78.3
Gemini 4 Carbon
- 78.1 GPT-6 Astra
- 76.8 GPT-6.1 Sol
- 74.1 GPT-6.1 Terra
- 74.0 GPT-6 Sol
- 72.4
Gemini 4 Argon
- 71.2
Gemini 3.8 Flash
- 70.6 GPT-5.6 Sol
- 69.2
Gemini 3.1 Pro
- 67.5
Gemini 3.6 Flash
- 66.5 Claude Opus 5.5
- 64.8 GPT-6 Luna
- 63.8
Gemini 3 Flash
- 62.3 Grok 4.7
- 61.3 GPT-5.6 Terra
- 60.3
Qwen 3.8 Max
- 59.5 Grok 4.6
- 58.7 Claude Sonnet 5.5
- 56.9 GPT-5.6 Luna
- 53.5
Gemini 3.5 Flash-Lite
- 50.1
Gemma 4 31B IT
- 47.4
Gemma 4 26B A4B
- 47.3 Claude Haiku 5.5
- 1
Gemini 4 Carbon 78.3%
- 2 GPT-6 Astra 78.1%
- 3 GPT-6.1 Sol 76.8%
- 4 GPT-6.1 Terra 74.1%
- 5 GPT-6 Sol 74%
- 6
Gemini 4 Argon 72.4%
- 7
Gemini 3.8 Flash 71.2%
- 8 GPT-5.6 Sol 70.6%
- 9
Gemini 3.1 Pro 69.2%
- 10
Gemini 3.6 Flash 67.5%
- 11 Claude Opus 5.5 66.5%
- 12 GPT-6 Luna 64.8%
- 13
Gemini 3 Flash 63.8%
- 14 Grok 4.7 62.3%
- 15 GPT-5.6 Terra 61.3%
- 16
Qwen 3.8 Max 60.3%
- 17 Grok 4.6 59.5%
- 18 Claude Sonnet 5.5 58.7%
- 19 GPT-5.6 Luna 56.9%
- 20
Gemini 3.5 Flash-Lite 53.5%
- 21
Gemma 4 31B IT 50.1%
- 22
Gemma 4 26B A4B 47.4%
- 23 Claude Haiku 5.5 47.3%
Source: Official FACTS Benchmark Suite leaderboard
- 78.3
Gemini 4 Carbon
- 78.1 GPT-6 Astra
- 76.8 GPT-6.1 Sol
- 74.1 GPT-6.1 Terra
- 74.0 GPT-6 Sol
- 72.4
Gemini 4 Argon
- 71.2
Gemini 3.8 Flash
- 70.6 GPT-5.6 Sol
- 70.5
Gemini 3 Pro
- 69.2
Gemini 3.1 Pro
- 67.5
Gemini 3.6 Flash
- 66.5 Claude Opus 5.5
- 64.8 GPT-6 Luna
- 63.8
Gemini 3 Flash
- 62.3 Grok 4.7
- 61.3 GPT-5.6 Terra
- 60.3
Qwen 3.8 Max
- 59.5 Grok 4.6
- 58.7 Claude Sonnet 5.5
- 58.6 GLM-5V-Turbo
- 56.9 GPT-5.6 Luna
- 53.5
Gemini 3.5 Flash-Lite
- 50.1
Gemma 4 31B IT
- 47.4
Gemma 4 26B A4B
- 47.3 Claude Haiku 5.5
- 40.6
Gemini 3.1 Flash-Lite
- 1
Gemini 4 Carbon 78.3%
- 2 GPT-6 Astra 78.1%
- 3 GPT-6.1 Sol 76.8%
- 4 GPT-6.1 Terra 74.1%
- 5 GPT-6 Sol 74%
- 6
Gemini 4 Argon 72.4%
- 7
Gemini 3.8 Flash 71.2%
- 8 GPT-5.6 Sol 70.6%
- 9
Gemini 3 Pro 70.5%
- 10
Gemini 3.1 Pro 69.2%
- 11
Gemini 3.6 Flash 67.5%
- 12 Claude Opus 5.5 66.5%
- 13 GPT-6 Luna 64.8%
- 14
Gemini 3 Flash 63.8%
- 15 Grok 4.7 62.3%
- 16 GPT-5.6 Terra 61.3%
- 17
Qwen 3.8 Max 60.3%
- 18 Grok 4.6 59.5%
- 19 Claude Sonnet 5.5 58.7%
- 20 GLM-5V-Turbo 58.6%
- 21 GPT-5.6 Luna 56.9%
- 22
Gemini 3.5 Flash-Lite 53.5%
- 23
Gemma 4 31B IT 50.1%
- 24
Gemma 4 26B A4B 47.4%
- 25 Claude Haiku 5.5 47.3%
- 26
Gemini 3.1 Flash-Lite 40.6%
Sources: Official FACTS Benchmark Suite leaderboard and labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 78.3% | Oct 2026 | Official leaderboard | |
| 2 | GPT-6 Astra | 78.1% | Sep 2026 | Official leaderboard | |
| 3 | GPT-6.1 Sol | 76.8% | Sep 2026 | Official leaderboard | |
| 4 | GPT-6.1 Terra | 74.1% | Oct 2026 | Official leaderboard | |
| 5 | GPT-6 Sol | 74% | Sep 2026 | Official leaderboard | |
| 6 | | 72.4% | Sep 2026 | Official leaderboard | |
| 7 | | 71.2% | Sep 2026 | Official leaderboard | |
| 8 | GPT-5.6 Sol | 70.6% | Jul 2026 | Official leaderboard | |
| 9 | | 69.2% | Feb 2026 | Official leaderboard | |
| 10 | | 67.5% | Jul 2026 | Official leaderboard | |
| 11 | Claude Opus 5.5 | 66.5% | Sep 2026 | Official leaderboard | |
| 12 | GPT-6 Luna | 64.8% | Sep 2026 | Official leaderboard | |
| 13 | | 63.8% | Dec 2025 | Official leaderboard | |
| 14 | Grok 4.7 | 62.3% | Sep 2026 | Official leaderboard | |
| 15 | GPT-5.6 Terra | 61.3% | Jul 2026 | Official leaderboard | |
| 16 | | 60.3% | Aug 2026 | Official leaderboard | |
| 17 | Grok 4.6 | 59.5% | Aug 2026 | Official leaderboard | |
| 18 | Claude Sonnet 5.5 | 58.7% | Sep 2026 | Official leaderboard | |
| 19 | GPT-5.6 Luna | 56.9% | Jul 2026 | Official leaderboard | |
| 20 | | 53.5% | Jul 2026 | Official leaderboard | |
| 21 | | 50.1% | Apr 2026 | Official leaderboard | |
| 22 | | 47.4% | Apr 2026 | Official leaderboard | |
| 23 | Claude Haiku 5.5 | 47.3% | Oct 2026 | Official leaderboard |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 78.3% | Oct 2026 | Official leaderboard | |
| 2 | GPT-6 Astra | 78.1% | Sep 2026 | Official leaderboard | |
| 3 | GPT-6.1 Sol | 76.8% | Sep 2026 | Official leaderboard | |
| 4 | GPT-6.1 Terra | 74.1% | Oct 2026 | Official leaderboard | |
| 5 | GPT-6 Sol | 74% | Sep 2026 | Official leaderboard | |
| 6 | | 72.4% | Sep 2026 | Official leaderboard | |
| 7 | | 71.2% | Sep 2026 | Official leaderboard | |
| 8 | GPT-5.6 Sol | 70.6% | Jul 2026 | Official leaderboard | |
| 9 | | 70.5% | Nov 2025 | Lab model card | |
| 10 | | 69.2% | Feb 2026 | Official leaderboard | |
| 11 | | 67.5% | Jul 2026 | Official leaderboard | |
| 12 | Claude Opus 5.5 | 66.5% | Sep 2026 | Official leaderboard | |
| 13 | GPT-6 Luna | 64.8% | Sep 2026 | Official leaderboard | |
| 14 | | 63.8% | Dec 2025 | Official leaderboard | |
| 15 | Grok 4.7 | 62.3% | Sep 2026 | Official leaderboard | |
| 16 | GPT-5.6 Terra | 61.3% | Jul 2026 | Official leaderboard | |
| 17 | | 60.3% | Aug 2026 | Official leaderboard | |
| 18 | Grok 4.6 | 59.5% | Aug 2026 | Official leaderboard | |
| 19 | Claude Sonnet 5.5 | 58.7% | Sep 2026 | Official leaderboard | |
| 20 | GLM-5V-Turbo | 58.6% | Apr 2026 | Lab model card | |
| 21 | GPT-5.6 Luna | 56.9% | Jul 2026 | Official leaderboard | |
| 22 | | 53.5% | Jul 2026 | Official leaderboard | |
| 23 | | 50.1% | Apr 2026 | Official leaderboard | |
| 24 | | 47.4% | Apr 2026 | Official leaderboard | |
| 25 | Claude Haiku 5.5 | 47.3% | Oct 2026 | Official leaderboard | |
| 26 | | 40.6% | Mar 2026 | Lab model card |
Labs report scores for 4 models in their cards. Switch the scores above to rank them together.
6 models have held number one on the official leaderboard since its release in December 2025 5 models have held number one with claims included since its release in December 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.
Sources: Official FACTS Benchmark Suite leaderboard and labs’ model cards
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official FACTS Benchmark Suite leaderboard and labs’ model cards
Labs headline it less often now, last in April 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 |
|---|---|---|---|
| Apr 1, 2026 | GLM-5V-Turbo | 58.6 | Facts 58.6 |
| Mar 3, 2026 | 40.6 | FACTS Benchmark Suite | |
| Dec 17, 2025 | 61.9 | FACTS Benchmark Suite | |
| Nov 18, 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 |
|---|---|---|---|---|
| Mar 3, 2026 | | 40.6 | FACTS Benchmark Suite (results table, p.4) | Hallucination |
One lab claim next to the leaderboard
Each score a lab reported for its own model, beside the same model's best score on the leaderboard. Gaps mostly come from different harnesses, effort levels and task subsets. Every benchmark.
| Model | Lab says | Board says | Gap |
|---|---|---|---|
| | 61.9% FACTS Benchmark Suite | 63.8% Leaderboard | −1.9 pts |
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%
- 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. GPT-6 Astra 4.20%
- Internal adversarially-designed factuality evaluations In use Models answer adversarial questions in nine categories such as history, math and code. Scored on agreement with human ideal responses. GPT-6 Luna 7.60%
- AA-Omniscience v1.1 In use Models answer 3,000 knowledge questions in six domains without tools and may abstain. The index adds correct answers and subtracts wrong ones.
Inkling 2 Small 16.1