---
title: "PlotGen-Bench — paper by Zhao et al. (2026)"
description: "Vision-language models can already read and reason about charts, but their ability to write runnable code that recreates a… 31 references."
authors: ["Yi Zhao", "Zhen Yang", "Shuaiqi Duan", "Wenmeng Yu", "Zhe Su", "Jibing Gong", "Jie Tang"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1375/"
doi: "10.18653/v1/2026.findings-acl.1375"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhao-2026-plotgen-bench-evaluating-vlms-generating/"
---
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# PlotGen-Bench: Evaluating VLMs on Generating Visualization Code from Diverse Plots across Multiple Libraries

**Authors:** [Yi Zhao](https://md.research.elamur.ai/free/en/authors/yi-zhao/), [Zhen Yang](https://md.research.elamur.ai/free/en/authors/zhen-yang/), [Shuaiqi Duan](https://md.research.elamur.ai/free/en/authors/shuaiqi-duan/), [Wenmeng Yu](https://md.research.elamur.ai/free/en/authors/wenmeng-yu/), [Zhe Su](https://md.research.elamur.ai/free/en/authors/zhe-su/), Jibing Gong and [Jie Tang](https://md.research.elamur.ai/free/en/authors/jie-tang/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1375/) · [PDF](https://aclanthology.org/2026.findings-acl.1375.pdf) · [DOI 10.18653/v1/2026.findings-acl.1375](https://doi.org/10.18653/v1/2026.findings-acl.1375) · [OpenAlex](https://openalex.org/W7166865126)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Vision-language models can already read and reason about charts, but their ability to write runnable code that recreates a given plot has been tested only narrowly. Earlier benchmarks such as ChartMimic and Plot2Code mostly use static 2D figures, Matplotlib alone, and simple reproduction. PlotGen-Bench fills this gap. It gathers plots from library tutorials and galleries, Stack Overflow, Kaggle, Zhihu, and arXiv papers. The collection includes 3D, interactive, and animated (GIF) visualizations.

Models are tested on three tasks:
- recreating a plot,
- converting it into another chart type while keeping the data,
- producing it in a specified library.

Scoring combines automatic checks (execution, library compliance, visual similarity) with a VLM acting as judge, and this judge was validated against human ratings.

Open-source models produce code that runs about as often as closed-source models' code, but the resulting images match the target less well. Every model struggles with reasoning-heavy work such as chart-type conversion and animation. Scores also drop as plot complexity grows, and GPT-5 is the strongest model overall. The benchmark gives a more realistic test bed for VLMs that write visualization code.

## Key points
- **Scope:** 9 high-level categories and 30 subcategories of plots. These cover 2D, 3D, interactive, and animated plots across 5 Python libraries: Matplotlib, Seaborn, Plotly, Plotnine, and NetworkX. The paper also mentions 28 plot types in places, which is inconsistent with the 30 subcategories.
- **Tasks:** Plot Replication, Plot Transformation (e.g., bar → line), and Multi-library Generation.
- **Plot Transformation results:** execution rates are high, but GPT-scores are much lower. This points to a gap between code that runs and plots that are semantically correct.
- **Human validation:** three blind annotators rated 100 samples on a 5-point Likert scale. Agreement with the VLM judge was 90% overall, highest for chart type accuracy (95%) and lowest for element completeness (63%).
- **Availability:** data and code are released at plotgen.github.io.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

📚 Shelves (3): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,730 works

## Cite

Cite as: Yi Zhao, Zhen Yang, Shuaiqi Duan, Wenmeng Yu, Zhe Su, Jibing Gong and Jie Tang. 2026. PlotGen-Bench: Evaluating VLMs on Generating Visualization Code from Diverse Plots across Multiple Libraries. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhao2026plotgen,
  title = {{PlotGen-Bench: Evaluating VLMs on Generating Visualization Code from Diverse Plots across Multiple Libraries}},
  author = {Yi Zhao, Zhen Yang, Shuaiqi Duan, Wenmeng Yu, Zhe Su, Jibing Gong and Jie Tang},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1375},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1375}
}
```

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*Updated 2 November 2026*