TextVQA
TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer. Introduced to benchmark VQA models' ability to read and reason about text within images, particularly for assistive technologies for visually impaired users. The dataset addresses the gap where existing VQA datasets had few text-based questions or were too small.
Qwen2-VL-72B-Instruct from Alibaba Cloud / Qwen Team currently leads the TextVQA leaderboard with a score of 0.855 across 15 evaluated AI models.
Qwen2-VL-72B-Instruct leads with 85.5%, followed by Qwen2.5 VL 7B Instruct at 84.9% and Qwen2.5-Omni-7B at 84.4%.
Progress Over Time
Interactive timeline showing model performance evolution on TextVQA
TextVQA Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 73B | — | — | ||
| 2 | Alibaba Cloud / Qwen Team | 8B | — | — | ||
| 3 | Alibaba Cloud / Qwen Team | 7B | — | — | ||
| 4 |  DeepSeek | 27B | — | — | ||
| 5 | DeepSeek | 16B | — | — | ||
| 6 |  Amazon | — | — | — | ||
| 7 | DeepSeek | 3B | — | — | ||
| 8 | Amazon | — | — | — | ||
| 9 |  xAI | — | — | — | ||
| 10 |  Microsoft | 6B | — | — | ||
| 11 | 90B | — | — | |||
| 12 | Microsoft | 4B | — | — | ||
| 13 |  Google | 12B | — | — | ||
| 14 | Google | 27B | — | — | ||
| 15 | Google | 4B | — | — |
FAQ
Common questions about TextVQA.
More evaluations to explore
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