multimodal benchmark
DocVQA is a Visual Question Answering benchmark on document images containing 50,000 questions defined on 12,000+ document images. The benchmark focuses on understanding document structure and content to answer questions about various document types including letters, memos, notes, and reports from the UCSF Industry Documents Library.
Updated Aug 7, 2026
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | AC | 97.1% | 100.0% | 11 | C | |
| 2 | AC | 96.9% | 90.0% | 11 | C | |
| 3 | AC | 96.5% | 80.0% | 11 | C | |
| 4 | AC | 96.5% | 70.0% | 11 | C | |
| 5 | AC | 96.1% | 60.0% | 11 | C | |
| 6 | AC | 96.1% | 50.0% | 11 | C | |
| 7 | AC | 95.3% | 40.0% | 11 | C | |
| 8 | AC | 95.3% | 30.0% | 11 | C | |
| 9 | AC | 95.0% | 20.0% | 11 | C | |
| 10 | AC | 95.0% | 10.0% | 11 | C | |
| 11 | AC | 94.2% | 0.0% | 11 | C |
Top published rows on the benchmark's original scale.
The top published results on this benchmark's own scale.
Definition and scoring fields from the benchmark registry.
DocVQA is a Visual Question Answering benchmark on document images containing 50,000 questions defined on 12,000+ document images. The benchmark focuses on understanding document structure and content to answer questions about various document types including letters, memos, notes, and reports from the UCSF Industry Documents Library.
Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
Common questions about DocVQAtest.
Qwen3 VL 235B A22B Instruct is currently ranked first with 97.1%.
DocVQA is a Visual Question Answering benchmark on document images containing 50,000 questions defined on 12,000+ document images. The benchmark focuses on understanding document structure and content to answer questions about various document types including letters, memos, notes, and reports from the UCSF Industry Documents Library.
Yes. Higher values rank better for this benchmark.
11 unique published model results are currently shown.
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.