multimodal benchmark
CharXiv-D is the descriptive questions subset of the CharXiv benchmark, designed to assess multimodal large language models' ability to extract basic information from scientific charts. It contains descriptive questions covering information extraction, enumeration, pattern recognition, and counting across 2,323 diverse charts from arXiv papers, all curated and verified by human experts.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | BY | 95.5% | 100.0% | 16 | C | |
| 02 | BY | 94.6% | 93.3% | 16 | C | |
| 03 | AC | 90.5% | 86.7% | 16 | C | |
| 04 | AC | 90.2% | 80.0% | 16 | C | |
| 05 | OP | 90.0% | 73.3% | 16 | C | |
| 06 | OP | 88.4% | 66.7% | 16 | C | |
| 07 | CO | 88.0% | 60.0% | 16 | C | |
| 08 | OP | 87.9% | 53.3% | 16 | C | |
| 09 | AC | 86.9% | 46.7% | 16 | C | |
| 10 | AC | 85.9% | 40.0% | 16 | C | |
| 11 | AC | 85.5% | 33.3% | 16 | C | |
| 12 | OP | 85.3% | 26.7% | 16 | C | |
| 13 | AC | 83.9% | 20.0% | 16 | C | |
| 14 | AC | 83.0% | 13.3% | 16 | C | |
| 15 | AC | 76.2% | 6.7% | 16 | C | |
| 16 | OP | 73.9% | 0.0% | 16 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What CharXiv-D measures and how its scores work.
CharXiv-D is the descriptive questions subset of the CharXiv benchmark, designed to assess multimodal large language models' ability to extract basic information from scientific charts. It contains descriptive questions covering information extraction, enumeration, pattern recognition, and counting across 2,323 diverse charts from arXiv papers, all curated and verified by human experts.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about CharXiv-D.
Seed 2.1 Pro is currently ranked first with 95.5%.
CharXiv-D is the descriptive questions subset of the CharXiv benchmark, designed to assess multimodal large language models' ability to extract basic information from scientific charts. It contains descriptive questions covering information extraction, enumeration, pattern recognition, and counting across 2,323 diverse charts from arXiv papers, all curated and verified by human experts.
Yes. Higher values rank better for this benchmark.
16 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.