Benchmark Almanac

FACTS Search

Gemini 3.1 Pro 85.6% leads since Feb 2026 Not in model cards

Models answer information-seeking questions with a web search tool (the Brave Search API) and must combine what they find correctly.

890 public and 994 private questions. Part of the FACTS Benchmark Suite. Kaggle later replaced this board with FACTS Search V2.

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 May 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
Short answer A number, word or short string matched to a key.
31
Models scored on its own leaderboard

Gemini 3.1 Pro leads with 85.6% among models released by May 2026

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.

The newest model with a score here came out in May 2026, and nobody has tested a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:

  1. 1 Gemini 3.1 Pro 85.6%
  2. 2 GPT-5.5 85%
  3. 3 Gemini 3 Flash 81%
  4. 4 Claude Opus 4.8 80.3%
  5. 5 Claude Opus 4.7 78.8%
  6. 6 GPT-5 77.7%
  7. 7 Grok 4.1 Fast reasoning 77.4%
  8. 8 Claude Opus 4.6 76.7%
  9. 9 Claude Sonnet 4.6 76.4%
  10. 10 Grok 4 75.3%
  11. 11 o3 74.8%
  12. 12 Claude Opus 4.5 73.3%
  13. 13 GPT-5.2 72.2%
  14. 14 Claude Sonnet 4.5 thinking 69.8%
  15. 15 Gemma 4 31B IT 68.9%
  16. 16 GPT-5 mini 67.9%
  17. 17 Grok 4 Fast reasoning 67.3%
  18. 18 Gemini 3.1 Flash-Lite 66.8%
  19. 19 Claude Sonnet 4 66.3%
  20. 20 o4-mini 66.2%
  21. 21 Claude Opus 4.1 65%
  22. 22 GPT-4.1 64.6%
  23. 23 Gemini 2.5 Pro Jun 2025 63.9%
  24. 24 Gemini 3.5 Flash 62.6%
  25. 25 GPT-5.1 62.4%
  26. 26 Gemini 2.5 Flash Sep 2025 60%
  27. 27 GPT-4o Mar 2025 56.8%
  28. 28 Gemma 4 26B A4B 56.7%
  29. 29 gpt-oss-120b 44.5%
  30. 30 Gemma 3 27B 41.4%
  31. 31 gpt-oss-20b 34.8%
#ModelScoreSettingDateSource
1 Gemini 3.1 Pro 85.6% Feb 2026 Official leaderboard
2 GPT-5.5 85% Apr 2026 Official leaderboard
3 Gemini 3 Flash 81% Dec 2025 Official leaderboard
4 Claude Opus 4.8 80.3% May 2026 Official leaderboard
5 Claude Opus 4.7 78.8% Apr 2026 Official leaderboard
6 GPT-5 77.7% Aug 2025 Official leaderboard
7 Grok 4.1 Fast 77.4% reasoning Nov 2025 Official leaderboard
8 Claude Opus 4.6 76.7% Feb 2026 Official leaderboard
9 Claude Sonnet 4.6 76.4% Feb 2026 Official leaderboard
10 Grok 4 75.3% Jul 2025 Official leaderboard
11 o3 74.8% Apr 2025 Official leaderboard
12 Claude Opus 4.5 73.3% Nov 2025 Official leaderboard
13 GPT-5.2 72.2% Dec 2025 Official leaderboard
14 Claude Sonnet 4.5 69.8% thinking Sep 2025 Official leaderboard
15 Gemma 4 31B IT 68.9% Apr 2026 Official leaderboard
16 GPT-5 mini 67.9% Aug 2025 Official leaderboard
17 Grok 4 Fast 67.3% reasoning Sep 2025 Official leaderboard
18 Gemini 3.1 Flash-Lite 66.8% Mar 2026 Official leaderboard
19 Claude Sonnet 4 66.3% May 2025 Official leaderboard
20 o4-mini 66.2% Apr 2025 Official leaderboard
21 Claude Opus 4.1 65% Aug 2025 Official leaderboard
22 GPT-4.1 64.6% Apr 2025 Official leaderboard
23 Gemini 2.5 Pro Jun 2025 63.9% Jun 2025 Official leaderboard
24 Gemini 3.5 Flash 62.6% May 2026 Official leaderboard
25 GPT-5.1 62.4% Nov 2025 Official leaderboard
26 Gemini 2.5 Flash Sep 2025 60% Sep 2025 Official leaderboard
27 GPT-4o Mar 2025 56.8% Mar 2025 Official leaderboard
28 Gemma 4 26B A4B 56.7% Apr 2026 Official leaderboard
29 gpt-oss-120b 44.5% Aug 2025 Official leaderboard
30 Gemma 3 27B 41.4% Mar 2025 Official leaderboard
31 gpt-oss-20b 34.8% Aug 2025 Official leaderboard

3 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 Passed 85% in Feb 2026, by Gemini 3.1 Pro

The best score passed 85% in Feb 2026

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 3.1 Pro Gemini 3 Flash GPT-5 o3 GPT-4.1 GPT-4o Gemma 3 27B 0% 20% 40% 60% 80% 100% 2025 2026 Near the ceiling (85%) Gemini 3.1 Pro GPT-5

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.

OpenAI Google DeepMind
FACTS Search V2 Passed 85% in Jul 2026, by GPT-5.6 Sol
GPT-6 Astra 89.9%
FACTS Search Passed 85% in Feb 2026, by Gemini 3.1 Pro
FACTS Search V2 Passed 85% in Jul 2026, by GPT-5.6 Sol
GPT-6 Astra 89.9%

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%