Charts are ubiquitous, as people often use them to analyze data, answer
questions, and discover critical insights. However, performing complex
analytical tasks with charts requires significant perceptual and cognitive
effort. Chart Question Answering (CQA) systems automate this process by
enabling models to interpret and reason with visual representations of data.
However, existing benchmarks like ChartQA lack real-world diversity and have
recently shown performance saturation with modern large vision-language models
(LVLMs). To address these limitations, we introduce ChartQAPro, a new benchmark
that includes 1,341 charts from 157 diverse sources, spanning various chart
types, including infographics and dashboards, and featuring 1,948 questions in
various types, such as multiple-choice, conversational, hypothetical, and
unanswerable questions, to better reflect real-world challenges. Our
evaluations with 21 models show a substantial performance drop for LVLMs on
ChartQAPro; e.g., Claude Sonnet 3.5 scores 90.5% on ChartQA but only 55.81% on
ChartQAPro, underscoring the complexity of chart reasoning. We complement our
findings with detailed error analyses and ablation studies, identifying key
challenges and opportunities for advancing LVLMs in chart understanding and
reasoning. We release ChartQAPro at https://github.com/vis-nlp/ChartQAPro.