Reasoning Benchmark

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.

Model scores 13 models tested

Reset
# 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

What now?

You've seen how models score on GPQA — here's what to do next.