---
title: "From Charts to Code — paper by Tang et al. (2026)"
description: "Existing chart-to-code benchmarks such as ChartMimic and Plot2Code are close to saturation: GPT-4o… 41 references, cited by 3 papers here."
authors: ["Jiahao Tang", "Henry Hengyuan Zhao", "Lijian Wu", "Zijian Zhang", "Yifei Tao", "Dongxing Mao", "Yang Wan", "Jingru Tan", "Min Zeng", "Min Li", "Alex Jinpeng Wang"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.616/"
doi: "10.18653/v1/2026.acl-long.616"
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/tang-2026-charts-code-hierarchical-benchmark-multimodal/"
---
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# From Charts to Code: A Hierarchical Benchmark for Multimodal Models

**Authors:** Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, [Zijian Zhang](https://md.research.elamur.ai/free/en/authors/zijian-zhang/), Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, [Min Zeng](https://md.research.elamur.ai/free/en/authors/min-zeng/), [Min Li](https://md.research.elamur.ai/free/en/authors/min-li/) and [Alex Jinpeng Wang](https://md.research.elamur.ai/free/en/authors/alex-jinpeng-wang/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/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.acl-long.616/) · [PDF](https://aclanthology.org/2026.acl-long.616.pdf) · [DOI 10.18653/v1/2026.acl-long.616](https://doi.org/10.18653/v1/2026.acl-long.616) · [OpenAlex](https://openalex.org/W4416054668)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/tang-2026-charts-code-hierarchical-benchmark-multimodal/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary

Existing chart-to-code benchmarks such as ChartMimic and Plot2Code are close to saturation: GPT-4o already scores above 80 on ChartMimic. They also mostly test simple reproduction, not the editing and raw-data plotting that users actually need. The authors address this with Chart2Code, a benchmark where a large multimodal model (LMM) must write executable Python plotting code. Its input is a reference chart, a natural-language instruction and, optionally, data supplied as raw text, a spreadsheet screenshot or a structured file such as Excel.

Tasks are organized into three levels of rising difficulty:

* **Level 1:** reproducing a chart.
* **Level 2:** editing a chart, e.g., switching the chart type or adding elements.
* **Level 3:** building a faithful chart from a long, unprocessed table.

Evaluation combines code-level scoring with assessment of the rendered image. Across 29 proprietary and open-source LMMs, models handle reproduction reasonably well but degrade sharply on editing and long-table tasks. Producing plausible code proves far easier than producing a visually faithful chart. The benchmark therefore exposes practical gaps that earlier, saturated benchmarks hide.

## Key points

- Chart2Code contains 2,186 tasks covering 22 chart types. Code and data are publicly released.
- The 29 evaluated LMMs include GPT-5.2, Gemini-3-Pro, Claude-Sonnet-4, Qwen3-VL, InternVL3/3.5, MiMo-VL and Seed-1.6-VL.
- On Level 2 editing, the strongest model, GPT-5.2, averages 72.21 on code-based metrics but only 33.41 on chart-quality (LMM-score) assessment.
- On Level 3, leading models obtain solid code-level scores (Gemini-3-Pro: 84.00 visual, 96.60 type). Their chart-level scores, however, stay roughly between 16 and 36. The main error sources are data extraction, style, layout and axes.
- Limitations: all tasks are in English only, and scoring relies on LLM/LMM judges, which may be biased; human comparison studies are provided in the paper's appendix.

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

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## Cite

Cite as: Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li and Alex Jinpeng Wang. 2026. From Charts to Code: A Hierarchical Benchmark for Multimodal Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{tang2026charts,
  title = {{From Charts to Code: A Hierarchical Benchmark for Multimodal Models}},
  author = {Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li and Alex Jinpeng Wang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.616},
  url = {https://doi.org/10.18653/v1/2026.acl-long.616}
}
```

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*Updated 1 November 2026*