DocVQA Benchmark Leaderboard
A dataset for Visual Question Answering on document images containing 50,000 questions defined on 12,000+ document images. The benchmark tests AI's ability to understand document structure and content, requiring models to comprehend document layout and perform information retrieval to answer questions about document images.
Qwen2.5 VL 72B Instruct from Alibaba Cloud / Qwen Team currently leads the DocVQA leaderboard with a score of 0.964 across 26 evaluated AI models.
Qwen2.5 VL 72B Instruct leads with 96.4%, followed by Qwen2.5 VL 7B Instruct at 95.7% and 
Claude 3.5 Sonnet at 95.2%.
Progress Over Time
Interactive timeline showing model performance evolution on DocVQA
DocVQA Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 72B | — | — | ||
| 2 | Alibaba Cloud / Qwen Team | 8B | — | — | ||
| 3 | Anthropic | — | 200K | $3.00 / $15.00 | ||
| 3 | Alibaba Cloud / Qwen Team | 7B | — | — | ||
| 5 |  Mistral AI | 24B | — | — | ||
| 6 | Alibaba Cloud / Qwen Team | 34B | — | — | ||
| 7 |  Meta | 400B | 1.0M | $0.17 / $0.60 | ||
| 7 | Meta | 109B | 10.0M | $0.08 / $0.30 | ||
| 9 |  xAI | — | 128K | $2.00 / $10.00 | ||
| 10 |  Amazon | — | 300K | $0.80 / $3.20 | ||
| 11 | Mistral AI | 124B | 128K | $2.00 / $6.00 | ||
| 11 |  DeepSeek | 27B | — | — | ||
| 13 | xAI | — | — | — | ||
| 13 |  Microsoft | 6B | 128K | $0.05 / $0.10 | ||
| 15 |  OpenAI | — | 128K | $2.50 / $10.00 | ||
| 16 | Amazon | — | 300K | $0.06 / $0.24 | ||
| 17 | DeepSeek | 16B | — | — | ||
| 18 | Mistral AI | 12B | 128K | $0.15 / $0.15 | ||
| 19 | 90B | 128K | $0.35 / $0.40 | |||
| 20 | DeepSeek | 3B | — | — | ||
| 21 | 11B | 128K | $0.05 / $0.05 | |||
| 22 |  Google | 12B | 131K | $0.05 / $0.10 | ||
| 23 | Google | 27B | 131K | $0.10 / $0.20 | ||
| 24 | xAI | — | — | — | ||
| 24 | xAI | — | — | — | ||
| 26 | Google | 4B | 131K | $0.02 / $0.04 |
FAQ
Common questions about DocVQA.
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