InfoVQAtest

InfoVQA test set with infographic images requiring joint reasoning over document layout, textual content, graphical elements, and data visualizations with elementary reasoning and arithmetic skills

Kimi K2.5 from Moonshot AI currently leads the InfoVQAtest leaderboard with a score of 0.926 across 12 evaluated AI models.

Paper
About this benchmark

What InfoVQAtest measures

InfoVQAtest is a multimodal benchmark that evaluates large language models on multimodal and vision tasks. LLM Stats tracks 12 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.9, with the leader reaching 0.9.

Compare leaders on the best AI for multimodal and best AI for vision leaderboards.

Publication

Paper
InfographicVQA
Authors
Minesh Mathew, Viraj Bagal, Rubèn Pérez Tito, Dimosthenis Karatzas, and 2 others
Published

Abstract

Infographics are documents designed to effectively communicate information using a combination of textual, graphical and visual elements. In this work, we explore the automatic understanding of infographic images by using Visual Question Answering technique.To this end, we present InfographicVQA, a new dataset that comprises a diverse collection of infographics along with natural language questions and answers annotations. The collected questions require methods to jointly reason over the document layout, textual content, graphical elements, and data visualizations. We curate the dataset with emphasis on questions that require elementary reasoning and basic arithmetic skills. Finally, we evaluate two strong baselines based on state of the art multi-modal VQA models, and establish baseline performance for the new task. The dataset, code and leaderboard will be made available at http://docvqa.org

Moonshot AIKimi K2.5 leads with 92.6%, followed by Alibaba Cloud / Qwen TeamQwen3 VL 235B A22B Thinking at 89.5% and Alibaba Cloud / Qwen TeamQwen3 VL 32B Thinking at 89.2%.

Progress Over Time

Interactive timeline showing model performance evolution on InfoVQAtest

State-of-the-art frontier
Open
Proprietary

InfoVQAtest Leaderboard

12 models
ContextCostLicense
1
Moonshot AI
Moonshot AI
1.0T
2
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
236B
3
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
33B
3
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
236B262K$0.30 / $1.50
5
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
33B
6
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B262K$0.18 / $2.09
6
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B
8
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
73B
9
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B262K$0.08 / $0.50
10
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B262K$0.10 / $1.00
11
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B
12
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B262K$0.10 / $0.60
Notice missing or incorrect data?

FAQ

Common questions about InfoVQAtest.

What is the InfoVQAtest benchmark?

InfoVQA test set with infographic images requiring joint reasoning over document layout, textual content, graphical elements, and data visualizations with elementary reasoning and arithmetic skills

What is the InfoVQAtest leaderboard?

The InfoVQAtest leaderboard ranks 12 AI models based on their performance on this benchmark. Currently, Kimi K2.5 by Moonshot AI leads with a score of 0.926. The average score across all models is 0.860.

What is the highest InfoVQAtest score?

The highest InfoVQAtest score is 0.926, achieved by Kimi K2.5 from Moonshot AI.

How many models are evaluated on InfoVQAtest?

12 models have been evaluated on the InfoVQAtest benchmark, with 0 verified results and 12 self-reported results.

Where can I find the InfoVQAtest paper?

The InfoVQAtest paper is available at https://arxiv.org/abs/2104.12756. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does InfoVQAtest cover?

InfoVQAtest is categorized under multimodal and vision. The benchmark evaluates multimodal models.

What is the best open-source model on InfoVQAtest?

Kimi K2.5 by Moonshot AI is the top-ranked open-source model on InfoVQAtest, with a score of 0.926 (rank #1).

Which model offers the best value on InfoVQAtest?

Among models scoring within 10% of the leader, Qwen3 VL 8B Thinking from Alibaba Cloud / Qwen Team is the cheapest, at $0.18 per million input tokens with a score of 0.860.

How recent are the InfoVQAtest leaderboard results?

The InfoVQAtest leaderboard was last updated in June 2026 and currently includes 12 evaluated models.

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