Benchmark Almanac

FACTS Parametric V2

GPT-6 Astra 83.8% leads since October 2026
Not in model cards

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 October 27th, 2026 with 20 models. No changelog was found, so what changed is unknown. This follows the FACTS Search V2 pattern.

Released
October 2026
Kind
Short factual questions
Level
University
Scored
Graded by a model
Answers
Short answer
Tasks
Public, plus a private test set
Strongest model tested
Gemini 4 Carbon #2 on the capability index
Last model added
October 2026
Scores refreshed
November 12th, 2026

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)
27
Models scored on its own leaderboard

GPT-6 Astra leads with 83.8%

Best score per model from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best.

  1. 1 GPT-6 Astra 83.8%
  2. 2 GPT-6.1 Sol 83.8%
  3. 3 Gemini 4 Carbon 81.6%
  4. 4 GPT-5.6 Sol 81.1%
  5. 5 Gemini 3.1 Pro 79.4%
  6. 6 Gemini 3.8 Flash 78.7%
  7. 7 GPT-6 Sol 76.1%
  8. 8 Gemini 3.6 Flash 73.9%
  9. 9 Gemini 3 Flash 72.4%
  10. 10 Gemini 4 Argon 72.2%
  11. 11 GPT-6.1 Terra 70.8%
  12. 12 Claude Opus 5.5 58.7%
  13. 13 GPT-5.6 Terra 57.6%
  14. 14 GPT-6 Luna 57%
  15. 15 Grok 4.7 54.3%
  16. 16 GPT-5.6 Luna 50.6%
  17. 17 Claude Opus 5 48.8%
  18. 18 Grok 4.6 44.5%
  19. 19 Gemini 3.5 Flash-Lite 43%
  20. 20 Grok 4.5 42.5%
  21. 21 GLM-5.3 40.3%
  22. 22 Claude Sonnet 5.5 35.1%
  23. 23 Qwen 3.8 Max 30.8%
  24. 24 Claude Sonnet 5 20.5%
  25. 25 Claude Haiku 5.5 13.1%
  26. 26 Gemma 4 31B IT 6.91%
  27. 27 Gemma 4 26B A4B 5.66%

Source: Official FACTS Parametric V2 leaderboard

#ModelScoreSettingDateSource
1 GPT-6 Astra 83.8% Sep 2026 Official leaderboard
2 GPT-6.1 Sol 83.8% Sep 2026 Official leaderboard
3 Gemini 4 Carbon 81.6% Oct 2026 Official leaderboard
4 GPT-5.6 Sol 81.1% Jul 2026 Official leaderboard
5 Gemini 3.1 Pro 79.4% Feb 2026 Official leaderboard
6 Gemini 3.8 Flash 78.7% Sep 2026 Official leaderboard
7 GPT-6 Sol 76.1% Sep 2026 Official leaderboard
8 Gemini 3.6 Flash 73.9% Jul 2026 Official leaderboard
9 Gemini 3 Flash 72.4% Dec 2025 Official leaderboard
10 Gemini 4 Argon 72.2% Sep 2026 Official leaderboard
11 GPT-6.1 Terra 70.8% Oct 2026 Official leaderboard
12 Claude Opus 5.5 58.7% Sep 2026 Official leaderboard
13 GPT-5.6 Terra 57.6% Jul 2026 Official leaderboard
14 GPT-6 Luna 57% Sep 2026 Official leaderboard
15 Grok 4.7 54.3% Sep 2026 Official leaderboard
16 GPT-5.6 Luna 50.6% Jul 2026 Official leaderboard
17 Claude Opus 5 48.8% Jul 2026 Official leaderboard
18 Grok 4.6 44.5% Aug 2026 Official leaderboard
19 Gemini 3.5 Flash-Lite 43% Jul 2026 Official leaderboard
20 Grok 4.5 42.5% Jul 2026 Official leaderboard
21 GLM-5.3 40.3% Aug 2026 Official leaderboard
22 Claude Sonnet 5.5 35.1% Sep 2026 Official leaderboard
23 Qwen 3.8 Max 30.8% Aug 2026 Official leaderboard
24 Claude Sonnet 5 20.5% Jun 2026 Official leaderboard
25 Claude Haiku 5.5 13.1% Oct 2026 Official leaderboard
26 Gemma 4 31B IT 6.91% Apr 2026 Official leaderboard
27 Gemma 4 26B A4B 5.66% Apr 2026 Official leaderboard

Number one over time

Lab claims count from the day the card came out, independent runs from the model’s release.

OpenAI
Own leaderboard 1 leader
GPT-6 Astra 83.8%

Source: Official FACTS Parametric V2 leaderboard

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 2027 Near the ceiling (85%) GPT-6 Astra Gemini 3.1 Pro Gemini 3 Flash 0% 20% 40% 60% 80% 100% 2026 2027 Near the ceiling (85%) GPT-6 Astra Gemini 3 Flash

Source: Official FACTS Parametric V2 leaderboard

No lab model card reports it yet

We read the headline results of 522 launch posts from 52 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 OpenAI
FACTS Parametric V2 6 leaders
GPT-6 Astra 83.8%
FACTS Parametric 5 leaders
GPT-6 Astra 83.1%

Source: Official leaderboards

More in Factuality

All 74 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%
  • 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. GPT-6 Astra 4.20%
  • Internal adversarially-designed factuality evaluations In use Models answer adversarial questions in nine categories such as history, math and code. Scored on agreement with human ideal responses. GPT-6 Luna 7.60%
  • AA-Omniscience v1.1 In use Models answer 3,000 knowledge questions in six domains without tools and may abstain. The index adds correct answers and subtracts wrong ones. Inkling 2 Small 16.1