Benchmark Almanac

FACTS Multimodal

Gemini 3.8 Flash 50.7% leads since Sep 2026 Not in model cards

Models answer questions about input images and must combine what they see with world knowledge. A judge checks coverage and contradictions.

711 public and 811 private prompts. Part of the FACTS Benchmark Suite. Scores were the lowest of the four parts at launch.

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 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.
39
Models scored on its own leaderboard

Gemini 3.8 Flash leads with 50.7%

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 Gemini 3.8 Flash 50.7%
  2. 2 Gemini 3.7 Flash 49.8%
  3. 3 GPT-6.1 Sol 48.5%
  4. 4 Claude Opus 5.5 48.4%
  5. 5 GPT-6 Astra 47.7%
  6. 6 Gemini 3.6 Flash 47.2%
  7. 7 Gemini 2.5 Pro Jun 2025 46.9%
  8. 8 GPT-5.5 45.8%
  9. 9 GPT-5 44.1%
  10. 10 GPT-6 Sol 43.1%
  11. 11 GPT-5.1 41.8%
  12. 12 GPT-5 mini 41.5%
  13. 13 Gemini 3.5 Flash 41.4%
  14. 14 Gemini 3.1 Pro 41.3%
  15. 15 Gemini 3 Flash 41.3%
  16. 16 Gemini 2.5 Flash Sep 2025 41%
  17. 17 GPT-5.4 41%
  18. 18 GPT-4.1 40.1%
  19. 19 o3 39.9%
  20. 20 Claude Opus 4.8 39.8%
  21. 21 GPT-5.2 39.7%
  22. 22 Gemini 3.1 Flash-Lite 39.4%
  23. 23 Claude Opus 4.5 39.2%
  24. 24 Claude Opus 4.6 36.8%
  25. 25 Claude Opus 4.7 35.8%
  26. 26 Gemini 3.5 Flash-Lite 35.8%
  27. 27 o4-mini 34.5%
  28. 28 Claude Opus 4.1 33.1%
  29. 29 Claude Sonnet 4.6 32.5%
  30. 30 Claude Sonnet 4.5 thinking 31.1%
  31. 31 Claude Sonnet 4 28.6%
  32. 32 Grok 4 25.7%
  33. 33 Gemma 4 31B IT 24.7%
  34. 34 Gemma 3 27B 24.6%
  35. 35 Gemma 4 26B A4B 23.9%
  36. 36 Mistral Small 3.1 21.2%
  37. 37 Gemma 3 12B 20.8%
  38. 38 Grok 4 Fast reasoning 17.7%
  39. 39 Gemma 3 4B 13.6%
#ModelScoreSettingDateSource
1 Gemini 3.8 Flash 50.7% Sep 2026 Official leaderboard
2 Gemini 3.7 Flash 49.8% Aug 2026 Official leaderboard
3 GPT-6.1 Sol 48.5% Sep 2026 Official leaderboard
4 Claude Opus 5.5 48.4% Sep 2026 Official leaderboard
5 GPT-6 Astra 47.7% Sep 2026 Official leaderboard
6 Gemini 3.6 Flash 47.2% Jul 2026 Official leaderboard
7 Gemini 2.5 Pro Jun 2025 46.9% Jun 2025 Official leaderboard
8 GPT-5.5 45.8% Apr 2026 Official leaderboard
9 GPT-5 44.1% Aug 2025 Official leaderboard
10 GPT-6 Sol 43.1% Sep 2026 Official leaderboard
11 GPT-5.1 41.8% Nov 2025 Official leaderboard
12 GPT-5 mini 41.5% Aug 2025 Official leaderboard
13 Gemini 3.5 Flash 41.4% May 2026 Official leaderboard
14 Gemini 3.1 Pro 41.3% Feb 2026 Official leaderboard
15 Gemini 3 Flash 41.3% Dec 2025 Official leaderboard
16 Gemini 2.5 Flash Sep 2025 41% Sep 2025 Official leaderboard
17 GPT-5.4 41% Mar 2026 Official leaderboard
18 GPT-4.1 40.1% Apr 2025 Official leaderboard
19 o3 39.9% Apr 2025 Official leaderboard
20 Claude Opus 4.8 39.8% May 2026 Official leaderboard
21 GPT-5.2 39.7% Dec 2025 Official leaderboard
22 Gemini 3.1 Flash-Lite 39.4% Mar 2026 Official leaderboard
23 Claude Opus 4.5 39.2% Nov 2025 Official leaderboard
24 Claude Opus 4.6 36.8% Feb 2026 Official leaderboard
25 Claude Opus 4.7 35.8% Apr 2026 Official leaderboard
26 Gemini 3.5 Flash-Lite 35.8% Jul 2026 Official leaderboard
27 o4-mini 34.5% Apr 2025 Official leaderboard
28 Claude Opus 4.1 33.1% Aug 2025 Official leaderboard
29 Claude Sonnet 4.6 32.5% Feb 2026 Official leaderboard
30 Claude Sonnet 4.5 31.1% thinking Sep 2025 Official leaderboard
31 Claude Sonnet 4 28.6% May 2025 Official leaderboard
32 Grok 4 25.7% Jul 2025 Official leaderboard
33 Gemma 4 31B IT 24.7% Apr 2026 Official leaderboard
34 Gemma 3 27B 24.6% Mar 2025 Official leaderboard
35 Gemma 4 26B A4B 23.9% Apr 2026 Official leaderboard
36 Mistral Small 3.1 21.2% Mar 2025 Official leaderboard
37 Gemma 3 12B 20.8% Mar 2025 Official leaderboard
38 Grok 4 Fast 17.7% reasoning Sep 2025 Official leaderboard
39 Gemma 3 4B 13.6% Mar 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.

Google DeepMind
Own leaderboard 4 leaders

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% 2025 2026 Near the ceiling (85%) Gemini 3.8 Flash Gemini 2.5 Pro GPT-4.1 Gemma 3 27B 0% 20% 40% 60% 80% 100% 2025 2026 Near the ceiling (85%) Gemini 3.8 Flash Gemini 2.5 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.

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%