Benchmark
ChartQA
reasoning
vision
multimodal
multimodal
ChartQA is a large-scale benchmark comprising 9.6K human-written questions and 23.1K questions generated from human-written chart summaries, designed to evaluate models' abilities in visual and logical reasoning over charts.
语言EN
满分1
参评模型24
模型排名
| 名次 | 模型 | 机构 | 分数 | 来源 |
|---|---|---|---|---|
| 1 | Claude 3.5 Sonnet | Anthropic | 90.8 | 来源 ↗ |
| 2 | Llama 4 Maverick | Meta | 90.0 | 来源 ↗ |
| 3 | Qwen2.5 VL 72B Instruct | Alibaba Cloud / Qwen Team | 89.5 | 来源 ↗ |
| 4 | Nova Pro | Amazon | 89.2 | 来源 ↗ |
| 5 | Llama 4 Scout | Meta | 88.8 | 来源 ↗ |
| 6 | Qwen2-VL-72B-Instruct | Alibaba Cloud / Qwen Team | 88.3 | 来源 ↗ |
| 7 | Pixtral Large | Mistral AI | 88.1 | 来源 ↗ |
| 8 | Mistral Small 3.2 24B Instruct | Mistral AI | 87.4 | 来源 ↗ |
| 9 | Qwen2.5 VL 7B Instruct | Alibaba Cloud / Qwen Team | 87.3 | 来源 ↗ |
| 10 | Nova Lite | Amazon | 86.8 | 来源 ↗ |
| 11 | DeepSeek VL2 | DeepSeek | 86.0 | 来源 ↗ |
| 12 | GPT-4o | OpenAI | 85.7 | 来源 ↗ |
| 13 | Llama 3.2 90B Instruct | Meta | 85.5 | 来源 ↗ |
| 14 | Qwen2.5-Omni-7B | Alibaba Cloud / Qwen Team | 85.3 | 来源 ↗ |
| 15 | DeepSeek VL2 Small | DeepSeek | 84.5 | 来源 ↗ |
| 16 | Llama 3.2 11B Instruct | Meta | 83.4 | 来源 ↗ |
| 17 | Pixtral-12B | Mistral AI | 81.8 | 来源 ↗ |
| 18 | Phi-3.5-vision-instruct | Microsoft | 81.8 | 来源 ↗ |
| 19 | Phi-4-multimodal-instruct | Microsoft | 81.4 | 来源 ↗ |
| 20 | DeepSeek VL2 Tiny | DeepSeek | 81.0 | 来源 ↗ |
| 21 | Gemma 3 27B | 78.0 | 来源 ↗ | |
| 22 | Grok-1.5V | xAI | 76.1 | 来源 ↗ |
| 23 | Gemma 3 12B | 75.7 | 来源 ↗ | |
| 24 | Gemma 3 4B | 68.8 | 来源 ↗ |