Document Understanding Benchmark

OmniDocBench 1.5

OmniDocBench 1.5 - document understanding and parsing benchmark.

Category

Document Understanding

Score type

percent

Scale

100%

higher is better

Models tested

16

What is OmniDocBench 1.5?

OmniDocBench 1.5 is a benchmark for reading and parsing real documents. The official description says: "OmniDocBench 1.5 - document understanding and parsing benchmark". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

OmniDocBench 1.5 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: OmniDocBench 1.5 - document understanding and parsing benchmark.

Frequently asked questions

What is the OmniDocBench 1.5 benchmark?

OmniDocBench 1.5 is a benchmark for reading and parsing real documents. The official description says: 'OmniDocBench 1.5 - document understanding and parsing benchmark'. Below we describe in simple words what benchmarks of this type generally examine.

What does OmniDocBench 1.5 test?

OmniDocBench 1.5 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 OmniDocBench 1.5 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 OmniDocBench 1.5 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/omnidocbench-1-5/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 16 models tested

Reset
# Model Score Date Source Verified
1 MiniMax-M3
MiniMaxAI
91.6%
11.06.2026 MiniMax-M3 model card benchmark table Unverified
2 Qwen3.8-27B
Qwen Team (Alibaba Cloud)
91.1%
18.08.2026 Report Verified
3 Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
89.9%
16.04.2026 Qwen Team (official model card) Verified
4 Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
89.8%
01.02.2026 Report Verified
5 Qwen3.6-27B
Qwen Team (Alibaba Cloud)
89.4%
18.08.2026 Report Unverified
6 Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
89.3%
16.04.2026 Qwen Team (official model card - comparison) Unverified
7 Qwen3.5-27B
Qwen Team (Alibaba Cloud)
88.9%
16.04.2026 Qwen Team (official model card - comparison) Unverified
8 Qwen3.5-4B
Qwen
86.2%
01.02.2026 Report Verified
9 Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
84.5%
01.02.2026 Report Unverified
10 Gemma4-31B-it
Google
80.1%
16.04.2026 Qwen Team (official model card - comparison) Unverified
11 Muse-Glimmer-30B
Meta Superintelligence Lab
75.8%
01.08.2026 Meta Superintelligence Lab (official model card) Verified
12 Gemma4-26B-A4B
Google
74.4%
16.04.2026 Qwen Team (official model card - comparison) Unverified
13 Gemma3-27B
Google
0.365%
02.07.2026 Report Unverified
14 Gemma4-E2B
Google
0.29%
02.07.2026 Report Verified
15 Gemma4-E4B
Google
0.181%
02.07.2026 Report Verified
16 Gemma4-12B
Google
0.164%
02.07.2026 Report Verified

What now?

You've seen how models score on OmniDocBench 1.5 — here's what to do next.