Chinese SimpleQA
3,000 short Chinese factual questions across six topics. Graded correct, incorrect or not attempted.
GitHub repo moved from OpenStellarTeam to LivingFutureLab.
- Made by
- Alibaba (Taobao & Tmall)
- Released
- November 2024
- Kind
- Short factual questions
- Level
- University
- Scored
- Graded by a model
- Answers
- Short answer
- Tasks
- All public
- Strongest model tested
-
DeepSeek-V4-Pro #69 on the capability index
- Last in a launch post
- April 2026, dropped after it saturated
- Last model added
- June 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
New LLM evaluation benchmarks are important to align with the rapid development of Large Language Models (LLMs). In this work, we present Chinese SimpleQA, the first comprehensive Chinese benchmark to evaluate the factuality ability of language models to answer short questions, and Chinese SimpleQA mainly has five properties (i.e., Chinese, Diverse, High-quality, Static, Easy-to-evaluate). Specifically, first, we focus on the Chinese language over 6 major topics with 99 diverse subtopics. Second, we conduct a comprehensive quality control process to achieve high-quality questions and answers, where the reference answers are static and cannot be changed over time. Third, following SimpleQA, the questions and answers are very short, and the grading process is easy-to-evaluate based on OpenAI API. Based on Chinese SimpleQA, we perform a comprehensive evaluation on the factuality abilities of existing LLMs. Finally, we hope that Chinese SimpleQA could guide the developers to better understand the Chinese factuality abilities of their models and facilitate the growth of foundation models.
ERNIE 5.0 leads with 86% among models released by April 2026
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.
o1-preview leads with 67.9% across all independent runs
Best score per model across the official leaderboard, Epoch AI’s own runs and any board Epoch AI copied. Each bar and row names its source.
ERNIE 5.0 leads with 86%, claims included
Best score per model from every source, including labs’ own numbers and claims Epoch AI collected from reports and papers. Labs pick their own settings, so a claim can beat an independent run of the same model.
The newest model with a score here came out in April 2026, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon
- GPT-6 Astra
- Claude Opus 5.5
- and 16 more
- 1
ERNIE 5.0 86%
- 2
DeepSeek-V4-Pro 84.4%
- 3
DeepSeek-V4-Flash 78.9%
- 4
ERNIE-4.5-300B-A47B 77.1%
- 5
Ling-2.6-1T 76.5%
- 6 GLM-5 75.2%
- 7
Hunyuan-TurboS 70.8%
- 8 GLM-4.5 70.1%
- 9
Hunyuan-T1 67.9%
- 10
MiniMax-Text-01 67.4%
- 11
DeepSeek-V3 64.8%
- 12
DeepSeek-R1 63.7%
- 13
ERNIE-4.5-21B-A3B-Thinking 49.1%
Source: Labs’ model cards
- 67.9 o1-preview
- 59.7 GPT-4o
- 56.7
Gemini 1.5 Pro
- 55.7
DeepSeek-V2.5
- 53.5 Claude 3.5 Sonnet
- 51.5
Yi-Large
- 50.2
Qwen2.5-72B
- 49.1 GPT-4 Turbo
- 47.4 GPT-4
- 47.0
DeepSeek LLM 67B
- 44.1 o1-mini
- 41.1
Qwen2.5-32B
- 40.2
Llama 3.1-70B
- 37.8 GPT-4o mini
- 37.2
Qwen2.5-14B
- 32.8
InternLM2.5-20B-Chat
- 31.8
Yi-1.5-34B
- 30.1 GPT-3.5 Turbo
- 27.9
Qwen2.5-7B
- 27.6 GLM-4-9B
- 21.8
Baichuan2-13B
- 17.7
Llama 3.1-8B
- 17.3
Qwen2.5-3B
- 15.6
Mistral-7B-Instruct-v0.2
- 14.0
Baichuan 2-7B
- 12.0 ChatGLM3-6B
- 12.0
Qwen2.5-1.5B
- 1 o1-preview 67.9%
- 2 GPT-4o Aug 2024 59.7%
- 3
Gemini 1.5 Pro Sep 2024 56.7%
- 4
DeepSeek-V2.5 Sep 2024 55.7%
- 5 Claude 3.5 Sonnet 53.5%
- 6
Yi-Large 51.5%
- 7
Qwen2.5-72B 50.2%
- 8 GPT-4 Turbo Apr 2024 49.1%
- 9 GPT-4 Jun 2023 47.4%
- 10
DeepSeek LLM 67B 47%
- 11 o1-mini 44.1%
- 12
Qwen2.5-32B 41.1%
- 13
Llama 3.1-70B 40.2%
- 14 GPT-4o mini 37.8%
- 15
Qwen2.5-14B 37.2%
- 16
InternLM2.5-20B-Chat 32.8%
- 17
Yi-1.5-34B 31.8%
- 18 GPT-3.5 Turbo Jan 2024 30.1%
- 19
Qwen2.5-7B 27.9%
- 20 GLM-4-9B 27.6%
- 21
Baichuan2-13B 21.8%
- 22
Llama 3.1-8B 17.7%
- 23
Qwen2.5-3B 17.3%
- 24
Mistral-7B-Instruct-v0.2 15.6%
- 25
Baichuan 2-7B 14%
- 26 ChatGLM3-6B 12%
- 27
Qwen2.5-1.5B 12%
Source: Official Chinese SimpleQA leaderboard
- 86.0
ERNIE 5.0
- 84.4
DeepSeek-V4-Pro
- 78.9
DeepSeek-V4-Flash
- 77.1
ERNIE-4.5-300B-A47B
- 76.5
Ling-2.6-1T
- 75.2 GLM-5
- 70.8
Hunyuan-TurboS
- 70.1 GLM-4.5
- 67.9 o1-preview
- 67.9
Hunyuan-T1
- 67.4
MiniMax-Text-01
- 64.8
DeepSeek-V3
- 63.7
DeepSeek-R1
- 59.7 GPT-4o
- 56.7
Gemini 1.5 Pro
- 55.7
DeepSeek-V2.5
- 53.5 Claude 3.5 Sonnet
- 51.5
Yi-Large
- 50.2
Qwen2.5-72B
- 49.1 GPT-4 Turbo
- 49.1
ERNIE-4.5-21B-A3B-Thinking
- 47.4 GPT-4
- 47.0
DeepSeek LLM 67B
- 44.1 o1-mini
- 41.1
Qwen2.5-32B
- 40.2
Llama 3.1-70B
- 37.8 GPT-4o mini
- 37.2
Qwen2.5-14B
- 32.8
InternLM2.5-20B-Chat
- 31.8
Yi-1.5-34B
- 30.1 GPT-3.5 Turbo
- 27.9
Qwen2.5-7B
- 27.6 GLM-4-9B
- 21.8
Baichuan2-13B
- 17.7
Llama 3.1-8B
- 17.3
Qwen2.5-3B
- 15.6
Mistral-7B-Instruct-v0.2
- 14.0
Baichuan 2-7B
- 12.0 ChatGLM3-6B
- 12.0
Qwen2.5-1.5B
- 1
ERNIE 5.0 86%
- 2
DeepSeek-V4-Pro 84.4%
- 3
DeepSeek-V4-Flash 78.9%
- 4
ERNIE-4.5-300B-A47B 77.1%
- 5
Ling-2.6-1T 76.5%
- 6 GLM-5 75.2%
- 7
Hunyuan-TurboS 70.8%
- 8 GLM-4.5 70.1%
- 9 o1-preview 67.9%
- 10
Hunyuan-T1 67.9%
- 11
MiniMax-Text-01 67.4%
- 12
DeepSeek-V3 64.8%
- 13
DeepSeek-R1 63.7%
- 14 GPT-4o Aug 2024 59.7%
- 15
Gemini 1.5 Pro Sep 2024 56.7%
- 16
DeepSeek-V2.5 Sep 2024 55.7%
- 17 Claude 3.5 Sonnet 53.5%
- 18
Yi-Large 51.5%
- 19
Qwen2.5-72B 50.2%
- 20 GPT-4 Turbo Apr 2024 49.1%
- 21
ERNIE-4.5-21B-A3B-Thinking 49.1%
- 22 GPT-4 Jun 2023 47.4%
- 23
DeepSeek LLM 67B 47%
- 24 o1-mini 44.1%
- 25
Qwen2.5-32B 41.1%
- 26
Llama 3.1-70B 40.2%
- 27 GPT-4o mini 37.8%
- 28
Qwen2.5-14B 37.2%
- 29
InternLM2.5-20B-Chat 32.8%
- 30
Yi-1.5-34B 31.8%
- 31 GPT-3.5 Turbo Jan 2024 30.1%
- 32
Qwen2.5-7B 27.9%
- 33 GLM-4-9B 27.6%
- 34
Baichuan2-13B 21.8%
- 35
Llama 3.1-8B 17.7%
- 36
Qwen2.5-3B 17.3%
- 37
Mistral-7B-Instruct-v0.2 15.6%
- 38
Baichuan 2-7B 14%
- 39 ChatGLM3-6B 12%
- 40
Qwen2.5-1.5B 12%
Sources: Labs’ model cards and official Chinese SimpleQA leaderboard
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 86% | Feb 2026 | Lab model card | |
| 2 | | 84.4% | Apr 2026 | Lab model card | |
| 3 | | 78.9% | Apr 2026 | Lab model card | |
| 4 | | 77.1% | Jun 2025 | Lab model card | |
| 5 | | 76.5% | Jun 2026 | Lab model card | |
| 6 | GLM-5 | 75.2% | Feb 2026 | Lab model card | |
| 7 | | 70.8% | Feb 2025 | Lab model card | |
| 8 | GLM-4.5 | 70.1% | Aug 2025 | Lab model card | |
| 9 | | 67.9% | Mar 2025 | Lab model card | |
| 10 | | 67.4% | Jan 2025 | Lab model card | |
| 11 | | 64.8% | Dec 2024 | Lab model card | |
| 12 | | 63.7% | Jan 2025 | Lab model card | |
| 13 | | 49.1% | Sep 2025 | Lab model card |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | o1-preview | 67.9% | Sep 2024 | Official leaderboard | |
| 2 | GPT-4o Aug 2024 | 59.7% | Aug 2024 | Official leaderboard | |
| 3 | | 56.7% | Sep 2024 | Official leaderboard | |
| 4 | | 55.7% | Sep 2024 | Official leaderboard | |
| 5 | Claude 3.5 Sonnet | 53.5% | Jun 2024 | Official leaderboard | |
| 6 | | 51.5% | May 2024 | Official leaderboard | |
| 7 | | 50.2% | Sep 2024 | Official leaderboard | |
| 8 | GPT-4 Turbo Apr 2024 | 49.1% | Apr 2024 | Official leaderboard | |
| 9 | GPT-4 Jun 2023 | 47.4% | Jun 2023 | Official leaderboard | |
| 10 | | 47% | Nov 2023 | Official leaderboard | |
| 11 | o1-mini | 44.1% | Sep 2024 | Official leaderboard | |
| 12 | | 41.1% | Sep 2024 | Official leaderboard | |
| 13 | | 40.2% | Jul 2024 | Official leaderboard | |
| 14 | GPT-4o mini | 37.8% | Jul 2024 | Official leaderboard | |
| 15 | | 37.2% | Sep 2024 | Official leaderboard | |
| 16 | | 32.8% | Aug 2024 | Official leaderboard | |
| 17 | | 31.8% | May 2024 | Official leaderboard | |
| 18 | GPT-3.5 Turbo Jan 2024 | 30.1% | Jan 2024 | Official leaderboard | |
| 19 | | 27.9% | Sep 2024 | Official leaderboard | |
| 20 | GLM-4-9B | 27.6% | Jun 2024 | Official leaderboard | |
| 21 | | 21.8% | Sep 2023 | Official leaderboard | |
| 22 | | 17.7% | Jul 2024 | Official leaderboard | |
| 23 | | 17.3% | Sep 2024 | Official leaderboard | |
| 24 | | 15.6% | Dec 2023 | Official leaderboard | |
| 25 | | 14% | Sep 2023 | Official leaderboard | |
| 26 | ChatGLM3-6B | 12% | Oct 2023 | Official leaderboard | |
| 27 | | 12% | Sep 2024 | Official leaderboard |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 86% | Feb 2026 | Lab model card | |
| 2 | | 84.4% | Apr 2026 | Lab model card | |
| 3 | | 78.9% | Apr 2026 | Lab model card | |
| 4 | | 77.1% | Jun 2025 | Lab model card | |
| 5 | | 76.5% | Jun 2026 | Lab model card | |
| 6 | GLM-5 | 75.2% | Feb 2026 | Lab model card | |
| 7 | | 70.8% | Feb 2025 | Lab model card | |
| 8 | GLM-4.5 | 70.1% | Aug 2025 | Lab model card | |
| 9 | o1-preview | 67.9% | Sep 2024 | Official leaderboard | |
| 10 | | 67.9% | Mar 2025 | Lab model card | |
| 11 | | 67.4% | Jan 2025 | Lab model card | |
| 12 | | 64.8% | Dec 2024 | Lab model card | |
| 13 | | 63.7% | Jan 2025 | Lab model card | |
| 14 | GPT-4o Aug 2024 | 59.7% | Aug 2024 | Official leaderboard | |
| 15 | | 56.7% | Sep 2024 | Official leaderboard | |
| 16 | | 55.7% | Sep 2024 | Official leaderboard | |
| 17 | Claude 3.5 Sonnet | 53.5% | Jun 2024 | Official leaderboard | |
| 18 | | 51.5% | May 2024 | Official leaderboard | |
| 19 | | 50.2% | Sep 2024 | Official leaderboard | |
| 20 | GPT-4 Turbo Apr 2024 | 49.1% | Apr 2024 | Official leaderboard | |
| 21 | | 49.1% | Sep 2025 | Lab model card | |
| 22 | GPT-4 Jun 2023 | 47.4% | Jun 2023 | Official leaderboard | |
| 23 | | 47% | Nov 2023 | Official leaderboard | |
| 24 | o1-mini | 44.1% | Sep 2024 | Official leaderboard | |
| 25 | | 41.1% | Sep 2024 | Official leaderboard | |
| 26 | | 40.2% | Jul 2024 | Official leaderboard | |
| 27 | GPT-4o mini | 37.8% | Jul 2024 | Official leaderboard | |
| 28 | | 37.2% | Sep 2024 | Official leaderboard | |
| 29 | | 32.8% | Aug 2024 | Official leaderboard | |
| 30 | | 31.8% | May 2024 | Official leaderboard | |
| 31 | GPT-3.5 Turbo Jan 2024 | 30.1% | Jan 2024 | Official leaderboard | |
| 32 | | 27.9% | Sep 2024 | Official leaderboard | |
| 33 | GLM-4-9B | 27.6% | Jun 2024 | Official leaderboard | |
| 34 | | 21.8% | Sep 2023 | Official leaderboard | |
| 35 | | 17.7% | Jul 2024 | Official leaderboard | |
| 36 | | 17.3% | Sep 2024 | Official leaderboard | |
| 37 | | 15.6% | Dec 2023 | Official leaderboard | |
| 38 | | 14% | Sep 2023 | Official leaderboard | |
| 39 | ChatGLM3-6B | 12% | Oct 2023 | Official leaderboard | |
| 40 | | 12% | Sep 2024 | Official leaderboard |
The benchmark's own leaderboard has 41 entries, 27 of them models we track. Switch the scores above to rank them together.
5 models have held number one in labs’ own numbers since its release in November 2024 Number one over time 4 models have held number one with claims included since its release in November 2024
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.
Sources: Official Chinese SimpleQA leaderboard and labs’ model cards
Baidu’s own number for ERNIE 5.0 passed 85% in February 2026
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official Chinese SimpleQA leaderboard and labs’ model cards
Labs headline it less often now, last in April 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.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| Apr 24, 2026 | 78.9 | Chinese-SimpleQA (Pass@1), V4-Flash Max | |
| Apr 24, 2026 | 84.4 | Chinese-SimpleQA (Pass@1) | |
| Feb 6, 2026 | 86 | ChineseSimpleQA | |
| Sep 9, 2025 | 49.1 | ChineseSimpleQA (Accuracy) | |
| Jun 30, 2025 | 77.1 | ChineseSimpleQA | |
| Mar 21, 2025 | 67.9 | C-SimpleQA | |
| Mar 16, 2025 | Chinese SimpleQA | ||
| Feb 27, 2025 | 70.8 | Chinese-SimpleQA | |
| Jan 20, 2025 | 63.7 | C-SimpleQA (Correct) | |
| Jan 15, 2025 | 67.4 | C-SimpleQA | |
| Dec 26, 2024 | 64.8 | C-SimpleQA (Correct) |
10 system cards report it, from 5 labs
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.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Jun 13, 2026 | | 76.5 | C-SimpleQA, correct (Table 5) | Capabilities |
| Apr 26, 2026 | | 84.4 | Chinese-SimpleQA (Pass@1), DS-V4-Pro Max (Table 6) | Hallucination |
| Apr 26, 2026 | | 78.9 | Chinese-SimpleQA (Pass@1), DeepSeek-V4-Flash Max (Table 7) | Hallucination |
| Feb 17, 2026 | GLM-5 technical report | 75.2 | Chinese SimpleQA, World Knowledge panel (Figure 11) | Hallucination |
| Feb 4, 2026 | | 86 | ChineseSimpleQA (Table 2) | Capabilities |
| Aug 8, 2025 | GLM-4.5 technical report | 70.1 | GLM-4.5-Base, Chinese-SimpleQA (EM) (Table 2) | Hallucination |
| Jun 29, 2025 | | 77.1 | ChineseSimpleQA (Table 5) | Hallucination |
| Jan 20, 2025 | | 63.7 | C-SimpleQA (Correct), DeepSeek-R1 (Table 8, same as final column of Table 3) | Hallucination |
| Jan 14, 2025 | | 67.4 | C-SimpleQA (Table 8) | Hallucination |
| Dec 26, 2024 | | 64.8 | C-SimpleQA (Correct), DeepSeek-V3 (Table 6) | Hallucination |
More 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