Multimodal Benchmark

ChartQA

ChartQA - answering questions about charts and plots.

Category

Multimodal

Score type

percent

Scale

100%

higher is better

Models tested

3

What is ChartQA?

ChartQA is a benchmark for handling several input types together (images, documents, text). The official description says: "ChartQA - answering questions about charts and plots". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

ChartQA measures handling several input types together (images, documents, text). In detail, it checks:

  • questions that mix text with images or document pages
  • reading and extracting information from screenshots and scans
  • understanding layout, titles, logos and figures in real documents
  • staying accurate when the text content alone is not enough

How to read the score

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Official methodology note: ChartQA - answering questions about charts and plots.

Frequently asked questions

What is the ChartQA benchmark?

ChartQA is a benchmark for handling several input types together (images, documents, text). The official description says: 'ChartQA - answering questions about charts and plots'. Below we describe in simple words what benchmarks of this type generally examine.

What does ChartQA test?

ChartQA mainly measures handling several input types together (images, documents, text). Concretely, it checks: questions that mix text with images or document pages; reading and extracting information from screenshots and scans; understanding layout, titles, logos and figures in real documents.

How is the ChartQA score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get ChartQA data and scores?

The official source is https://arxiv.org/abs/2203.10244. The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/chartqa/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 3 models tested

Reset
# Model Score Date Source Verified
1 Gemma 3 27B PT
Google DeepMind
76.3%
27.02.2025 Report Verified
2 Gemma 3 12B PT
Google DeepMind
74.7%
27.02.2025 Report Verified
3 Gemma 3 4B PT
Google DeepMind
63.6%
27.02.2025 Report Verified

What now?

You've seen how models score on ChartQA — here's what to do next.