Document Understanding Benchmark

AI2D_TEST

AI2D_TEST - grade-school science diagram understanding benchmark.

Category

Document Understanding

Score type

percent

Scale

100%

higher is better

Models tested

12

What is AI2D_TEST?

AI2D_TEST is a benchmark for reading and parsing real documents. The official description says: "AI2D_TEST - grade-school science diagram understanding benchmark". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

AI2D_TEST measures reading and parsing real documents. In detail, it checks:

  • extracting structured text from scans, PDFs and photos
  • keeping tables, headings and reading order intact
  • answering comprehension questions about the document content
  • handling noisy layouts, handwriting and multi-page documents

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: AI2D_TEST - grade-school science diagram understanding benchmark.

Frequently asked questions

What is the AI2D_TEST benchmark?

AI2D_TEST is a benchmark for reading and parsing real documents. The official description says: 'AI2D_TEST - grade-school science diagram understanding benchmark'. Below we describe in simple words what benchmarks of this type generally examine.

What does AI2D_TEST test?

AI2D_TEST mainly measures reading and parsing real documents. Concretely, it checks: extracting structured text from scans, PDFs and photos; keeping tables, headings and reading order intact; answering comprehension questions about the document content.

How is the AI2D_TEST 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 AI2D_TEST data and scores?

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/ai2d-test/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 12 models tested

Reset
# Model Score Date Source Verified
1 Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
93.3%
01.02.2026 Report Verified
2 Qwen3.5-27B
Qwen Team (Alibaba Cloud)
92.9%
16.04.2026 Qwen Team (official model card - comparison) Unverified
3 Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
92.7%
16.04.2026 Qwen Team (official model card) Verified
4 Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
92.6%
16.04.2026 Qwen Team (official model card - comparison) Unverified
5 Qwen3.5-4B
Qwen
89.6%
01.02.2026 Report Verified
6 Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
89.2%
01.02.2026 Report Unverified
7 Gemma4-31B-it
Google
89%
16.04.2026 Qwen Team (official model card - comparison) Unverified
8 Gemma4-26B-A4B
Google
88.3%
16.04.2026 Qwen Team (official model card - comparison) Unverified
9 Gemma 3 27B PT
Google DeepMind
79%
27.02.2025 Report Verified
10 d1-2B
LiquidAI
76.7%
— LiquidAI/d1-2B model card (performance table, "AI2D") Unverified
11 Gemma 3 12B PT
Google DeepMind
75.2%
27.02.2025 Report Verified
12 Gemma 3 4B PT
Google DeepMind
63.2%
27.02.2025 Report Verified

What now?

You've seen how models score on AI2D_TEST — here's what to do next.