FACTS Parametric V2
Models answer closed-book factoid questions from their own knowledge, without tools. A judge model grades each answer.
Kaggle renamed the benchmark to Facts V2 Parametric and published version 2 on 2026-10-27 with 20 models. No changelog was found, so what changed is unknown. This follows the FACTS Search V2 pattern.
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 83.8%
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.
- 83.8 GPT-6 Astra
- 83.8 GPT-6.1 Sol
- 81.1 GPT-5.6 Sol
- 78.7
Gemini 3.8 Flash
- 76.1 GPT-6 Sol
- 73.9
Gemini 3.6 Flash
- 72.4
Gemini 3 Flash
- 72.2
Gemini 4 Argon
- 58.7 Claude Opus 5.5
- 57.6 GPT-5.6 Terra
- 57.0 GPT-6 Luna
- 54.3 Grok 4.7
- 50.6 GPT-5.6 Luna
- 48.8 Claude Opus 5
- 44.5 Grok 4.6
- 43.0
Gemini 3.5 Flash-Lite
- 42.5 Grok 4.5
- 35.1 Claude Sonnet 5.5
- 20.5 Claude Sonnet 5
- 13.1 Claude Haiku 5.5
- 1 GPT-6 Astra 83.8%
- 2 GPT-6.1 Sol 83.8%
- 3 GPT-5.6 Sol 81.1%
- 4
Gemini 3.8 Flash 78.7%
- 5 GPT-6 Sol 76.1%
- 6
Gemini 3.6 Flash 73.9%
- 7
Gemini 3 Flash 72.4%
- 8
Gemini 4 Argon 72.2%
- 9 Claude Opus 5.5 58.7%
- 10 GPT-5.6 Terra 57.6%
- 11 GPT-6 Luna 57%
- 12 Grok 4.7 54.3%
- 13 GPT-5.6 Luna 50.6%
- 14 Claude Opus 5 48.8%
- 15 Grok 4.6 44.5%
- 16
Gemini 3.5 Flash-Lite 43%
- 17 Grok 4.5 42.5%
- 18 Claude Sonnet 5.5 35.1%
- 19 Claude Sonnet 5 20.5%
- 20 Claude Haiku 5.5 13.1%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-6 Astra | 83.8% | Sep 2026 | Official leaderboard | |
| 2 | GPT-6.1 Sol | 83.8% | Sep 2026 | Official leaderboard | |
| 3 | GPT-5.6 Sol | 81.1% | Jul 2026 | Official leaderboard | |
| 4 | | 78.7% | Sep 2026 | Official leaderboard | |
| 5 | GPT-6 Sol | 76.1% | Sep 2026 | Official leaderboard | |
| 6 | | 73.9% | Jul 2026 | Official leaderboard | |
| 7 | | 72.4% | Dec 2025 | Official leaderboard | |
| 8 | | 72.2% | Sep 2026 | Official leaderboard | |
| 9 | Claude Opus 5.5 | 58.7% | Sep 2026 | Official leaderboard | |
| 10 | GPT-5.6 Terra | 57.6% | Jul 2026 | Official leaderboard | |
| 11 | GPT-6 Luna | 57% | Sep 2026 | Official leaderboard | |
| 12 | Grok 4.7 | 54.3% | Sep 2026 | Official leaderboard | |
| 13 | GPT-5.6 Luna | 50.6% | Jul 2026 | Official leaderboard | |
| 14 | Claude Opus 5 | 48.8% | Jul 2026 | Official leaderboard | |
| 15 | Grok 4.6 | 44.5% | Aug 2026 | Official leaderboard | |
| 16 | | 43% | Jul 2026 | Official leaderboard | |
| 17 | Grok 4.5 | 42.5% | Jul 2026 | Official leaderboard | |
| 18 | Claude Sonnet 5.5 | 35.1% | Sep 2026 | Official leaderboard | |
| 19 | Claude Sonnet 5 | 20.5% | Jun 2026 | Official leaderboard | |
| 20 | Claude Haiku 5.5 | 13.1% | Oct 2026 | Official leaderboard |
Number one over time
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.
No lab model card reports it yet
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.
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%