CSimpleQA
Chinese SimpleQA is the first comprehensive Chinese benchmark to evaluate the factuality ability of language models to answer short questions. It contains 3,000 high-quality questions spanning 6 major topics with 99 diverse subtopics, designed to assess Chinese factual knowledge across humanities, science, engineering, culture, and society.
DeepSeek-V4-Pro-Max from DeepSeek currently leads the CSimpleQA leaderboard with a score of 0.844 across 7 evaluated AI models.
What CSimpleQA measures
CSimpleQA is a text benchmark that evaluates large language models on general and language tasks. LLM Stats tracks 7 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.8, with the leader reaching 0.8.
Compare leaders on the best AI for general and best AI for language leaderboards.
Publication
- Paper
- Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models
- Authors
- Yancheng He, Shilong Li, Jiaheng Liu, Yingshui Tan, and 14 others
- Published
- arXiv
- 2411.07140
Abstract
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.
DeepSeek-V4-Pro-Max leads with 84.4%, followed by 
Qwen3-235B-A22B-Instruct-2507 at 84.3% and Qwen3 VL 235B A22B Instruct at 83.4%.
Progress Over Time
Interactive timeline showing model performance evolution on CSimpleQA
CSimpleQA Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | DeepSeek | 1.6T | 1.0M | $1.74 / $3.48 | ||
| 2 |  Alibaba Cloud / Qwen Team | 235B | — | — | ||
| 3 | Alibaba Cloud / Qwen Team | 236B | 262K | $0.30 / $1.50 | ||
| 4 | DeepSeek | 284B | 1.0M | $0.14 / $0.28 | ||
| 5 |  Moonshot AI | 1.0T | — | — | ||
| 6 | Moonshot AI | 1.0T | — | — | ||
| 7 | DeepSeek | 671B | — | — |
FAQ
Common questions about CSimpleQA.
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