Benchmark Almanac

SimpleQA Hallucination Rate

ChatGPT agent 7.90% leads since July 2025
Dropped

Models answer 4,326 short fact-seeking questions. This metric is the share of questions answered incorrectly, so declining to answer is not penalized.

Google DeepMind later released SimpleQA Verified, a cleaned 1,000-question version.

Made by
OpenAI
Released
October 2024
Kind
Hallucination rates
Scored
Checked automatically
Answers
Short answer
Tasks
All public
Strongest model tested
GPT-5 #70 on the capability index
Last in a launch post
February 2025, dropped before it saturated
Last model added
October 2025

In the authors’ words

We present SimpleQA, a benchmark that evaluates the ability of language models to answer short, fact-seeking questions. We prioritized two properties in designing this eval. First, SimpleQA is challenging, as it is adversarially collected against GPT-4 responses. Second, responses are easy to grade, because questions are created such that there exists only a single, indisputable answer. Each answer in SimpleQA is graded as either correct, incorrect, or not attempted. A model with ideal behavior would get as many questions correct as possible while not attempting the questions for which it is not confident it knows the correct answer. SimpleQA is a simple, targeted evaluation for whether models "know what they know," and our hope is that this benchmark will remain relevant for the next few generations of frontier models. SimpleQA can be found at https://github.com/openai/simple-evals.

Abstract of Measuring short-form factuality in large language models (arXiv:2411.04368, CC0)
12
Models scored in lab cards
1
Launch post lists it, from 1 lab
12
System cards report it, from 1 lab

ChatGPT agent leads with 7.90% among models released by August 2025

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

The newest model with a score here came out in August 2025, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:

  1. 1 ChatGPT agent 7.90%
  2. 2 GPT-5 mini 26%
  3. 3 GPT-5 nano 31%
  4. 4 GPT-4.5 37.1%
  5. 5 GPT-5 40%
  6. 6 o1-preview 44%
  7. 7 o1 44%
  8. 8 o3 51%
  9. 9 o1-mini 60%
  10. 10 gpt-oss-120b 78.2%
  11. 11 o4-mini 79%
  12. 12 gpt-oss-20b 91.4%

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 ChatGPT agent 7.90% Jul 2025 Lab model card
2 GPT-5 mini 26% Aug 2025 Lab model card
3 GPT-5 nano 31% Aug 2025 Lab model card
4 GPT-4.5 37.1% Feb 2025 Lab model card
5 GPT-5 40% Aug 2025 Lab model card
6 o1-preview 44% Sep 2024 Lab model card
7 o1 44% Dec 2024 Lab model card
8 o3 51% Apr 2025 Lab model card
9 o1-mini 60% Sep 2024 Lab model card
10 gpt-oss-120b 78.2% Aug 2025 Lab model card
11 o4-mini 79% Apr 2025 Lab model card
12 gpt-oss-20b 91.4% Aug 2025 Lab model card

3 models have held number one in labs’ own numbers since its release in October 2024

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

OpenAI
Lab claims Not tested since Oct 2025

Source: Labs’ model cards

Scores over time

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

Reported by the lab in its model card Best so far
0% 20% 40% 60% 80% 100% 2025 2026 ChatGPT agent GPT-4.5 o1-preview o1-mini 0% 20% 40% 60% 80% 100% 2025 2026 ChatGPT agent GPT-4.5

Source: Labs’ model cards

No launch post has listed it since February 2025

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.

OpenAI
DateLaunch postScoreAs the post writes it
Feb 27, 2025 GPT-4.5 37.1 SimpleQA Hallucination Rate (lower is better)

12 system cards report it, from 1 lab

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
Oct 27, 2025 GPT-5 system card addendum 52 SimpleQA, hallucination rate, gpt-5-oct-3 (Table 4) Hallucination
Aug 7, 2025 GPT-5 system card 40 SimpleQA (no web), hallucination rate (Table 8) Hallucination
Aug 7, 2025 GPT-5 nano system card 31 SimpleQA (no web), hallucination rate, gpt-5-thinking-nano (Table 8) Hallucination
Aug 7, 2025 GPT-5 mini system card 26 SimpleQA (no web), hallucination rate, gpt-5-thinking-mini (Table 8) Hallucination
Aug 5, 2025 gpt-oss-20b model card 91.4 SimpleQA, hallucination rate, no browsing (Table 9) Hallucination
Aug 5, 2025 gpt-oss-120b model card 78.2 SimpleQA, hallucination rate, no browsing (Table 9) Hallucination
Jul 17, 2025 ChatGPT agent system card 7.9 SimpleQA, hallucination rate, lower is better (Table 4) Hallucination
Apr 16, 2025 o4-mini system card 79 SimpleQA, hallucination rate, lower is better (Table 4) Hallucination
Apr 16, 2025 o3 system card 51 SimpleQA, hallucination rate, lower is better (Table 4) Hallucination
Dec 5, 2024 o1 system card 44 SimpleQA, hallucination rate, lower is better (Table 3) Hallucination
Sep 12, 2024 o1-preview system card 44 SimpleQA, hallucination rate, lower is better (Table 2) Hallucination
Sep 12, 2024 o1-mini system card 60 SimpleQA, hallucination rate, lower is better (Table 2) Hallucination

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