Benchmark Almanac

FACTS Benchmark Suite

GPT-6 Astra 75.4% leads since Sep 2026 Fading

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 .

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 Short factual questions Public tasks plus a private test set Last in a launch post Mar 2026 , dropped before it saturated Last model added Sep 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.
40
Models scored on its own leaderboard
3
Launch posts list it, from 1 lab
1
System card reports it, from 1 lab

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.

  1. 1 GPT-6 Astra 75.4%
  2. 2 GPT-6.1 Sol 74.6%
  3. 3 Gemini 3.8 Flash 68.5%
  4. 4 GPT-6 Sol 68.4%
  5. 5 Gemini 3.7 Flash 65.4%
  6. 6 Gemini 3 Flash 64.9%
  7. 7 Claude Opus 5.5 64%
  8. 8 Gemini 3.6 Flash 63.1%
  9. 9 GPT-6 Luna 62.3%
  10. 10 Gemini 3.1 Pro 60.6%
  11. 11 Claude Opus 4.8 59.7%
  12. 12 GPT-5.6 Sol 56.5%
  13. 13 Gemma 4 31B IT 56.2%
  14. 14 GPT-5.6 Luna 55.3%
  15. 15 Gemma 4 26B A4B 54%
  16. 16 Gemini 3.5 Flash 52.9%
  17. 17 GPT-5.6 Terra 50%
  18. 18 Gemini 3.5 Flash-Lite 47.4%
  19. 19 Gemini 2.5 Pro Jun 2025 46.9%
  20. 20 GPT-5.5 45.8%
  21. 21 GPT-5 44.1%
  22. 22 GPT-5.1 41.8%
  23. 23 GPT-5 mini 41.5%
  24. 24 Gemini 2.5 Flash Sep 2025 41%
  25. 25 Grok 4.5 40.6%
  26. 26 GPT-4.1 40.1%
  27. 27 o3 39.9%
  28. 28 GPT-5.2 39.7%
  29. 29 Gemini 3.1 Flash-Lite 39.4%
  30. 30 Claude Opus 4.5 39.2%
  31. 31 Claude Opus 4.6 36.8%
  32. 32 Claude Opus 4.7 35.8%
  33. 33 o4-mini 34.5%
  34. 34 Claude Opus 4.1 33.1%
  35. 35 Claude Sonnet 4.6 32.5%
  36. 36 Claude Sonnet 4.5 thinking 31.1%
  37. 37 Claude Sonnet 4 28.6%
  38. 38 Grok 4 25.7%
  39. 39 Gemma 3 27B 24.6%
  40. 40 Grok 4 Fast reasoning 17.7%
#ModelScoreSettingDateSource
1 GPT-6 Astra 75.4% Sep 2026 Official leaderboard
2 GPT-6.1 Sol 74.6% Sep 2026 Official leaderboard
3 Gemini 3.8 Flash 68.5% Sep 2026 Official leaderboard
4 GPT-6 Sol 68.4% Sep 2026 Official leaderboard
5 Gemini 3.7 Flash 65.4% Aug 2026 Official leaderboard
6 Gemini 3 Flash 64.9% Dec 2025 Official leaderboard
7 Claude Opus 5.5 64% Sep 2026 Official leaderboard
8 Gemini 3.6 Flash 63.1% Jul 2026 Official leaderboard
9 GPT-6 Luna 62.3% Sep 2026 Official leaderboard
10 Gemini 3.1 Pro 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 Gemma 4 31B IT 56.2% Apr 2026 Official leaderboard
14 GPT-5.6 Luna 55.3% Jul 2026 Official leaderboard
15 Gemma 4 26B A4B 54% Apr 2026 Official leaderboard
16 Gemini 3.5 Flash 52.9% May 2026 Official leaderboard
17 GPT-5.6 Terra 50% Jul 2026 Official leaderboard
18 Gemini 3.5 Flash-Lite 47.4% Jul 2026 Official leaderboard
19 Gemini 2.5 Pro Jun 2025 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 Gemini 2.5 Flash Sep 2025 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 Gemini 3.1 Flash-Lite 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 Gemma 3 27B 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.

Google DeepMind OpenAI
Own leaderboard 5 leaders
GPT-6 Astra 75.4%
Lab claims Not tested since Mar 2026
Gemini 3 Pro 70.5%

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 Reported by the lab in its model card Best so far
0% 20% 40% 60% 80% 100% 2025 2026 Near the ceiling (85%) GPT-6 Astra Gemini 3 Pro Gemini 2.5 Pro GPT-4.1 Gemma 3 27B 0% 20% 40% 60% 80% 100% 2025 2026 Near the ceiling (85%) GPT-6 Astra Gemini 2.5 Pro

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.

Google DeepMind
DateLaunch postScoreAs the post writes it
3 Mar 2026 Gemini 3.1 Flash-Lite 40.6 FACTS Benchmark Suite
17 Dec 2025 Gemini 3 Flash 61.9 FACTS Benchmark Suite
18 Nov 2025 Gemini 3 Pro 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.

DateDocumentScoreAs the document writes itSection
3 Mar 2026 Gemini 3.1 Flash-Lite model card 40.6 FACTS Benchmark Suite (results table, p.4) Hallucination

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%