ChartQA
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.
Claude 3.5 Sonnet from Anthropic currently leads the ChartQA leaderboard with a score of 0.908 across 24 evaluated AI models.
What ChartQA measures
ChartQA is a multimodal benchmark that evaluates large language models on multimodal, reasoning, and vision tasks. LLM Stats tracks 24 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.8, with the leader reaching 0.9.
Compare leaders on the best AI for multimodal, best AI for reasoning and best AI for vision leaderboards.
Publication
- Paper
- ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
- Authors
- Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and 1 others
- Published
- arXiv
- 2203.10244
Abstract
Charts are very popular for analyzing data. When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations. They also commonly refer to visual features of a chart in their questions. However, most existing datasets do not focus on such complex reasoning questions as their questions are template-based and answers come from a fixed-vocabulary. In this work, we present a large-scale benchmark covering 9.6K human-written questions as well as 23.1K questions generated from human-written chart summaries. To address the unique challenges in our benchmark involving visual and logical reasoning over charts, we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions. While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark, the evaluation also reveals several challenges in answering complex reasoning questions.
Claude 3.5 Sonnet leads with 90.8%, followed by 
Llama 4 Maverick at 90.0% and 
Qwen2.5 VL 72B Instruct at 89.5%.
Progress Over Time
Interactive timeline showing model performance evolution on ChartQA
ChartQA Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Anthropic | — | — | — | ||
| 2 | Meta | 400B | — | — | ||
| 3 |  Alibaba Cloud / Qwen Team | 72B | — | — | ||
| 4 |  Amazon | — | — | — | ||
| 5 | Meta | 109B | — | — | ||
| 6 | Alibaba Cloud / Qwen Team | 73B | — | — | ||
| 7 |  Mistral AI | 124B | — | — | ||
| 8 | Mistral AI | 24B | — | — | ||
| 9 | Alibaba Cloud / Qwen Team | 8B | — | — | ||
| 10 | Amazon | — | — | — | ||
| 11 |  DeepSeek | 27B | — | — | ||
| 12 |  OpenAI | — | 128K | $2.50 / $10.00 | ||
| 13 | 90B | — | — | |||
| 14 | Alibaba Cloud / Qwen Team | 7B | — | — | ||
| 15 | DeepSeek | 16B | — | — | ||
| 16 | 11B | — | — | |||
| 17 | Mistral AI | 12B | — | — | ||
| 17 |  Microsoft | 4B | — | — | ||
| 19 | Microsoft | 6B | — | — | ||
| 20 | DeepSeek | 3B | — | — | ||
| 21 |  Google | 27B | — | — | ||
| 22 |  xAI | — | — | — | ||
| 23 | Google | 12B | — | — | ||
| 24 | Google | 4B | — | — |
FAQ
Common questions about ChartQA.
More evaluations to explore
Related benchmarks in the same category
A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.
A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.
Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions