FACTS Parametric
Models answer closed-book factoid questions from their own knowledge, without tools. A judge model grades each answer.
1,052 public and 1,052 private questions. Part of the FACTS Benchmark Suite.
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
- Short answer A number, word or short string matched to a key.
GPT-6 Astra leads with 83.1%
Best score per entry from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders.
- 83.1 GPT-6 Astra
- 82.1 GPT-6.1 Sol
- 80.7 GPT-5.6 Sol
- 79.0
Gemini 3.1 Pro
- 78.7
Gemini 3.7 Flash
- 78.0 GPT-5.5
- 77.3
Gemini 3.8 Flash
- 75.6 GPT-6 Sol
- 73.5
Gemini 3.5 Flash
- 73.3
Gemini 3.6 Flash
- 72.3
Gemini 3 Flash
- 71.5
Gemini 4 Argon
- 63.2
Gemini 2.5 Pro
- 58.6 Grok 4
- 57.9 Claude Opus 5.5
- 57.1 o3
- 56.7 GPT-5.6 Terra
- 55.8 GPT-5
- 51.5 GPT-4.1
- 48.8 Claude Opus 5
- 48.4
Gemini 3.1 Flash-Lite
- 46.8 Grok 4.5
- 43.4
Gemini 3.5 Flash-Lite
- 43.2 GPT-5.1
- 38.8 Claude Opus 4.7
- 33.2 Claude Opus 4.1
- 30.7
Gemini 2.5 Flash
- 30.6 Claude Opus 4.5
- 29.0 Claude Sonnet 4.5
- 25.1 Claude Opus 4.8
- 25.0 GPT-5.6 Luna
- 20.8
DeepSeek-R1
- 20.5 o4-mini
- 20.4 Claude Sonnet 4
- 16.1 GPT-5 mini
- 15.8 Grok 4 Fast
- 13.6 Qwen 3 235B A22B Instruct 2506
- 13.1 Claude Haiku 5.5
- 10.7 gpt-oss-120b
- 7.79
Gemma 4 31B IT
- 6.27
Gemma 3 27B
- 5.69
Gemma 4 26B A4B
- 5.47
Mistral Small 3.1
- 3.56
Gemma 3 12B
- 2.90 gpt-oss-20b
- 1.57
Gemma 3 4B
- 1 GPT-6 Astra 83.1%
- 2 GPT-6.1 Sol 82.1%
- 3 GPT-5.6 Sol 80.7%
- 4
Gemini 3.1 Pro 79%
- 5
Gemini 3.7 Flash 78.7%
- 6 GPT-5.5 78%
- 7
Gemini 3.8 Flash 77.3%
- 8 GPT-6 Sol 75.6%
- 9
Gemini 3.5 Flash 73.5%
- 10
Gemini 3.6 Flash 73.3%
- 11
Gemini 3 Flash 72.3%
- 12
Gemini 4 Argon 71.5%
- 13
Gemini 2.5 Pro Jun 2025 63.2%
- 14 Grok 4 58.6%
- 15 Claude Opus 5.5 57.9%
- 16 o3 57.1%
- 17 GPT-5.6 Terra 56.7%
- 18 GPT-5 55.8%
- 19 GPT-4.1 51.5%
- 20 Claude Opus 5 48.8%
- 21
Gemini 3.1 Flash-Lite 48.4%
- 22 Grok 4.5 46.8%
- 23
Gemini 3.5 Flash-Lite 43.4%
- 24 GPT-5.1 43.2%
- 25 Claude Opus 4.7 38.8%
- 26 Claude Opus 4.1 33.2%
- 27
Gemini 2.5 Flash Jun 2025 30.7%
- 28 Claude Opus 4.5 30.6%
- 29 Claude Sonnet 4.5 thinking 29%
- 30 Claude Opus 4.8 25.1%
- 31 GPT-5.6 Luna 25%
- 32
DeepSeek-R1 May 2025 20.8%
- 33 o4-mini 20.5%
- 34 Claude Sonnet 4 20.4%
- 35 GPT-5 mini 16.1%
- 36 Grok 4 Fast reasoning 15.8%
- 37 Qwen 3 235B A22B Instruct 2506 13.6%
- 38 Claude Haiku 5.5 13.1%
- 39 gpt-oss-120b 10.7%
- 40
Gemma 4 31B IT 7.79%
- 41
Gemma 3 27B 6.27%
- 42
Gemma 4 26B A4B 5.69%
- 43
Mistral Small 3.1 5.47%
- 44
Gemma 3 12B 3.56%
- 45 gpt-oss-20b 2.90%
- 46
Gemma 3 4B 1.57%
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-6 Astra | 83.1% | Sep 2026 | Official leaderboard | |
| 2 | GPT-6.1 Sol | 82.1% | Sep 2026 | Official leaderboard | |
| 3 | GPT-5.6 Sol | 80.7% | Jul 2026 | Official leaderboard | |
| 4 | | 79% | Feb 2026 | Official leaderboard | |
| 5 | | 78.7% | Aug 2026 | Official leaderboard | |
| 6 | GPT-5.5 | 78% | Apr 2026 | Official leaderboard | |
| 7 | | 77.3% | Sep 2026 | Official leaderboard | |
| 8 | GPT-6 Sol | 75.6% | Sep 2026 | Official leaderboard | |
| 9 | | 73.5% | May 2026 | Official leaderboard | |
| 10 | | 73.3% | Jul 2026 | Official leaderboard | |
| 11 | | 72.3% | Dec 2025 | Official leaderboard | |
| 12 | | 71.5% | Sep 2026 | Official leaderboard | |
| 13 | | 63.2% | Jun 2025 | Official leaderboard | |
| 14 | Grok 4 | 58.6% | Jul 2025 | Official leaderboard | |
| 15 | Claude Opus 5.5 | 57.9% | Sep 2026 | Official leaderboard | |
| 16 | o3 | 57.1% | Apr 2025 | Official leaderboard | |
| 17 | GPT-5.6 Terra | 56.7% | Jul 2026 | Official leaderboard | |
| 18 | GPT-5 | 55.8% | Aug 2025 | Official leaderboard | |
| 19 | GPT-4.1 | 51.5% | Apr 2025 | Official leaderboard | |
| 20 | Claude Opus 5 | 48.8% | Jul 2026 | Official leaderboard | |
| 21 | | 48.4% | Mar 2026 | Official leaderboard | |
| 22 | Grok 4.5 | 46.8% | Jul 2026 | Official leaderboard | |
| 23 | | 43.4% | Jul 2026 | Official leaderboard | |
| 24 | GPT-5.1 | 43.2% | Nov 2025 | Official leaderboard | |
| 25 | Claude Opus 4.7 | 38.8% | Apr 2026 | Official leaderboard | |
| 26 | Claude Opus 4.1 | 33.2% | Aug 2025 | Official leaderboard | |
| 27 | | 30.7% | Jun 2025 | Official leaderboard | |
| 28 | Claude Opus 4.5 | 30.6% | Nov 2025 | Official leaderboard | |
| 29 | Claude Sonnet 4.5 | 29% | thinking | Sep 2025 | Official leaderboard |
| 30 | Claude Opus 4.8 | 25.1% | May 2026 | Official leaderboard | |
| 31 | GPT-5.6 Luna | 25% | Jul 2026 | Official leaderboard | |
| 32 | | 20.8% | May 2025 | Official leaderboard | |
| 33 | o4-mini | 20.5% | Apr 2025 | Official leaderboard | |
| 34 | Claude Sonnet 4 | 20.4% | May 2025 | Official leaderboard | |
| 35 | GPT-5 mini | 16.1% | Aug 2025 | Official leaderboard | |
| 36 | Grok 4 Fast | 15.8% | reasoning | Sep 2025 | Official leaderboard |
| 37 | Qwen 3 235B A22B Instruct 2506 | 13.6% | Sep 2025 | Official leaderboard | |
| 38 | Claude Haiku 5.5 | 13.1% | Oct 2026 | Official leaderboard | |
| 39 | gpt-oss-120b | 10.7% | Aug 2025 | Official leaderboard | |
| 40 | | 7.79% | Apr 2026 | Official leaderboard | |
| 41 | | 6.27% | Mar 2025 | Official leaderboard | |
| 42 | | 5.69% | Apr 2026 | Official leaderboard | |
| 43 | | 5.47% | Mar 2025 | Official leaderboard | |
| 44 | | 3.56% | Mar 2025 | Official leaderboard | |
| 45 | gpt-oss-20b | 2.90% | Aug 2025 | Official leaderboard | |
| 46 | | 1.57% | Mar 2025 | Official leaderboard |
Labs report scores for 1 model 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.
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 Apr 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 |
|---|---|---|---|
| 24 Apr 2026 | 62.6 | FACTS Parametric (EM) |
Versions
We switch the top row to each new version when it comes out. Open it for one row per version. An old version stays in play while people still test it, so whoever first takes it past 85% gets credit for closing it out.
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%