Benchmark Almanac

FACTS Grounding v2

GPT-6 Astra 80.1% leads since Sep 2026 Not in model cards

Models write long answers from a supplied document of up to 32k tokens. Judge models check that each answer stays faithful to the document.

The FACTS paper says v2 uses much improved judge models, so scores do not compare with the January 2025 FACTS Grounding. It is the Grounding part of the FACTS Benchmark Suite. Kaggle published a third board version on 2026-10-23 with 15 models, with no changelog.

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 .

Abstract of The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality (arXiv:2512.10791, CC0)
Made by Google DeepMind + Google Research + Kaggle Released Dec 2025 Faithful to sources Public tasks plus a private test set Last model added Oct 2026 Scores refreshed 2 Nov 2026
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.
15
Models scored on its own leaderboard

GPT-6 Astra leads with 80.1%

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.

  1. 1 GPT-6 Astra 80.1%
  2. 2 GPT-6.1 Sol 78%
  3. 3 Gemma 4 26B A4B 77.8%
  4. 4 GPT-6 Luna 76.7%
  5. 5 Gemma 4 31B IT 75.8%
  6. 6 Qwen 3.8 Max 73.8%
  7. 7 Gemini 4 Argon 73.3%
  8. 8 Grok 4.7 68.6%
  9. 9 Gemini 3.8 Flash 68.2%
  10. 10 Claude Opus 5.5 62.5%
  11. 11 GPT-5.6 Terra 60.5%
  12. 12 Claude Haiku 5.5 57%
  13. 13 Gemini 3.1 Pro 56.6%
  14. 14 GLM-5.3-Flash 56.1%
  15. 15 gpt-oss-120b 20.1%
#ModelScoreSettingDateSource
1 GPT-6 Astra 80.1% Sep 2026 Official leaderboard
2 GPT-6.1 Sol 78% Sep 2026 Official leaderboard
3 Gemma 4 26B A4B 77.8% Apr 2026 Official leaderboard
4 GPT-6 Luna 76.7% Sep 2026 Official leaderboard
5 Gemma 4 31B IT 75.8% Apr 2026 Official leaderboard
6 Qwen 3.8 Max 73.8% Aug 2026 Official leaderboard
7 Gemini 4 Argon 73.3% Sep 2026 Official leaderboard
8 Grok 4.7 68.6% Sep 2026 Official leaderboard
9 Gemini 3.8 Flash 68.2% Sep 2026 Official leaderboard
10 Claude Opus 5.5 62.5% Sep 2026 Official leaderboard
11 GPT-5.6 Terra 60.5% Jul 2026 Official leaderboard
12 Claude Haiku 5.5 57% Oct 2026 Official leaderboard
13 Gemini 3.1 Pro 56.6% Feb 2026 Official leaderboard
14 GLM-5.3-Flash 56.1% Aug 2026 Official leaderboard
15 gpt-oss-120b 20.1% Aug 2025 Official leaderboard

4 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.

OpenAI Google DeepMind
Own leaderboard 4 leaders
GPT-6 Astra 80.1%

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

On the benchmark’s own leaderboard Best so far
0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) GPT-6 Astra Gemma 4 26B A4B Gemini 3.1 Pro gpt-oss-120b 0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) GPT-6 Astra Gemini 3.1 Pro

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.

Google DeepMind Moonshot AI OpenAI
FACTS Grounding v2 8 leaders
GPT-6 Astra 80.1%
FACTS Grounding Passed 85% in Feb 2025, by Gemini 2.0 Flash
Kimi K2 88.5%
FACTS Grounding v2 4 leaders
GPT-6 Astra 80.1%

More in Factuality

All 70 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%