GPQA
Google-Proof Q&A - graduate-level science questions. 5-shot.
Category
Reasoning
Score type
percent
Scale
100%
higher is better
Models tested
13
What is GPQA?
GPQA (Google-Proof Q&A) is a graduate-level science question set created by domain experts. Questions are written so that simply searching the web does not help: 'google-proof' means that even human experts with full search access only answer about a third of them correctly in the non-expert setting.
What does it test?
GPQA measures general reasoning and comprehension. In detail, it checks:
- deep science knowledge beyond common exam questions
- step-by-step scientific reasoning
- robustness against attractive wrong answers
- 'closed-book' problem solving without external help
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.
Frequently asked questions
What is the GPQA benchmark?
GPQA (Google-Proof Q&A) is a graduate-level science question set created by domain experts. Questions are written so that simply searching the web does not help: 'google-proof' means that even human experts with full search access only answer about a third of them correctly in the non-expert setting.
What does GPQA test?
GPQA mainly measures general reasoning and comprehension. Concretely, it checks: deep science knowledge beyond common exam questions; step-by-step scientific reasoning; robustness against attractive wrong answers.
How is the GPQA 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 GPQA data and scores?
The official source is https://arxiv.org/abs/2311.12022. 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/gpqa/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 13 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Qwen3-Next-80B-A3B-Thinking
Qwen
|
77.2%
|
10.09.2025 | Report | Verified |
| 2 |
Gemma4-12B
Google
|
72.8%
|
— | Report | Verified |
| 3 |
Qwen3-VL-30B-A3B
Qwen
|
70.4%
|
04.10.2025 | Report | Verified |
| 4 |
Spark-X2.5-4B
SparkLLM (XHToken)
|
67.4%
|
— | Report | Verified |
| 5 |
Qwen3.5-4B
Qwen
|
67.2%
|
— | Report | Verified |
| 6 |
ERNIE-5.5-Flash-Base
Baidu
|
60.2%
|
— | HF model card baidu/ERNIE-5.5-Flash-Base (EM, 5-shot, Unified Pipeline) | Unverified |
| 7 |
Gemma4-E4B
Google
|
54.5%
|
— | Report | Verified |
| 8 |
Qwen3.5-2B
Qwen Team (Alibaba Cloud)
|
44.6%
|
— | Report | Verified |
| 9 |
Spark-X2.5-1.7B
SparkLLM (XHToken)
|
43.8%
|
— | Report | Verified |
| 10 |
Gemma4-E2B
Google
|
43.8%
|
— | Report | Verified |
| 11 |
Gemma 3 12B PT
Google DeepMind
|
25.4%
|
27.02.2025 | Report | Verified |
| 12 |
Gemma 3 27B PT
Google DeepMind
|
24.3%
|
27.02.2025 | Report | Verified |
| 13 |
Gemma 3 4B PT
Google DeepMind
|
15%
|
27.02.2025 | Report | Verified |