C-Eval
C-Eval - comprehensive Chinese evaluation benchmark across 52 subjects.
Category
Knowledge
Score type
percent
Scale
100%
higher is better
Models tested
11
What is C-Eval?
C-Eval is a benchmark for broad factual and academic knowledge. The official description says: "C-Eval - comprehensive Chinese evaluation benchmark across 52 subjects". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
C-Eval measures broad factual and academic knowledge. In detail, it checks:
- facts and concepts from many academic fields at once
- harder variants with several plausible answer options
- knowledge that goes beyond the model's training-set surface forms
- resisting the plausible-but-wrong answers deliberately included
How to read the score
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Official methodology note: C-Eval - comprehensive Chinese evaluation benchmark across 52 subjects.
Frequently asked questions
What is the C-Eval benchmark?
C-Eval is a benchmark for broad factual and academic knowledge. The official description says: 'C-Eval - comprehensive Chinese evaluation benchmark across 52 subjects'. Below we describe in simple words what benchmarks of this type generally examine.
What does C-Eval test?
C-Eval mainly measures broad factual and academic knowledge. Concretely, it checks: facts and concepts from many academic fields at once; harder variants with several plausible answer options; knowledge that goes beyond the model's training-set surface forms.
How is the C-Eval score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.
Where do I get C-Eval data and scores?
The official source is https://cevalbenchmark.com/. The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/c-eval/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Official benchmark page / paper
The benchmark maintainer's website, paper or leaderboard
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 11 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
DeepSeek-V4-Pro
DeepSeek
|
93.1%
|
26.06.2026 | Report | Verified |
| 2 |
DeepSeek-V4-Flash
DeepSeek
|
92.1%
|
26.06.2026 | Report | Verified |
| 3 |
Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
|
92.1%
|
01.02.2026 | Report | Unverified |
| 4 |
Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
|
91.9%
|
01.02.2026 | Report | Verified |
| 5 |
Qwen3.6-27B
Qwen Team (Alibaba Cloud)
|
91.4%
|
22.04.2026 | Qwen Team (official model card) | Verified |
| 6 |
Qwen3.5-27B
Qwen Team (Alibaba Cloud)
|
90.5%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 7 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
90.2%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 8 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
90%
|
16.04.2026 | Qwen Team (official model card) | Verified |
| 9 |
Qwen3.5-4B
Qwen
|
85.1%
|
01.02.2026 | Report | Verified |
| 10 |
Gemma4-31B-it
Google
|
82.6%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 11 |
Gemma4-26B-A4B
Google
|
82.5%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |