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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<title>Chart2Code</title>
<meta name="description" content="From Charts to Code: A Hierarchical Benchmark for Multimodal Models" />
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<body>
<div class="page-container">
<section class="title-section">
<div class="content-wrapper title-wrapper">
<div class="title-header">
<h1 class="chart2code-title">
<span class="word-chart">Chart</span><span class="word-2">2</span><span class="word-code">Code</span>
</h1>
</div>
<h2 class="subtitle"><span class="subtitle-gradient">From Charts to Code</span>: A Hierarchical Benchmark for Multimodal Models</h2>
<div class="authors">
<span class="author-block"><a href="#">Jiahao Tang</a><sup>1</sup>,</span>
<span class="author-block"><a href="https://zhaohengyuan1.github.io/">Henry Hengyuan Zhao</a><sup>2</sup>,</span>
<span class="author-block"><a href="#">Lijian Wu</a><sup>1</sup>,</span>
<span class="author-block"><a href="#">Zijian Zhang</a><sup>1</sup>,</span>
<span class="author-block"><a href="#">Yifei Tao</a><sup>3</sup>,</span>
<span class="author-block"><a href="https://scholar.google.com/citations?user=RLVSYY0AAAAJ&hl=en">Dongxing Mao</a><sup>1</sup>,</span><br>
<span class="author-block"><a href="#">Yang Wan</a><sup>1</sup>,</span>
<span class="author-block"><a href="https://scholar.google.com/citations?user=l18d7kcAAAAJ&hl=en">Jingru Tan</a><sup>1</sup>,</span>
<span class="author-block"><a href="https://minzeng1990.github.io/">Min Zeng</a><sup>1</sup>,</span>
<span class="author-block"><a href="https://scholar.google.com.hk/citations?user=w47WJE4AAAAJ&hl=en">Min Li</a><sup>1</sup>,</span>
<span class="author-block"><a href="https://fingerrec.github.io/">Alex Jinpeng Wang</a><sup>1</sup></span>
</div>
<div class="affiliations">
<span class="author-block"><a href="https://github.com/CSU-JPG"><sup>1</sup>CSU-JPG, </a>Central South University,</span>
<span class="author-block"><sup>2</sup>National University of Singapore,</span>
<span class="author-block"><sup>3</sup>Nanyang Technological University,</span>
</div>
<div class="conference-badge">ACL 2026 Main</div>
<div class="button-group">
<a href="https://arxiv.org/abs/2510.17932"><button class="outline multimodal"><i class="fas fa-file-pdf"></i> Paper </button></a>
<a href="https://github.com/CSU-JPG/Chart2Code"><button class="outline multimodal"><i class="fa-brands fa-github"></i> Code </button></a>
<a href="https://huggingface.co/datasets/CSU-JPG/Chart2Code"><button class="outline multimodal"><i class="fas fa-images"></i> Data </button></a>
</div>
</div>
</section>
<section class="main-container">
<div class="content-wrapper">
<div class="content-box">
<h2 class="text-title">What's new with Chart2Code benchmark</h2>
<p class="text-content">TL;DR: We introduce Chart2Code, a new benchmark designed to evaluate chart generation capabilities of LMMs under progressively challenging conditions. There are five tasks in the Chart2Code benchmark.</p>
<div class="leaderboard-container">
<div class="tabs-container">
<div class="tab-links">
<button class="tab-link active" data-tab="dr-sample">Level 1: Direct Reproduction</button>
<button class="tab-link" data-tab="crd-sample">Level 1: Custom Raw Darta</button>
<button class="tab-link" data-tab="cfd-sample">Level 1: Custom Figure Data</button>
<button class="tab-link" data-tab="level2-sample">Level 2</button>
<button class="tab-link" data-tab="level3-sample">Level 3</button>
</div>
<div class="tab-content">
<div id="dr-sample" class="tab-pane active">
<img src="./assets/illustration_DR.png" alt="Direct Reproduction Example" class="tab-image">
</div>
<div id="crd-sample" class="tab-pane">
<img src="./assets/illustration_CRD.png" alt="Customized Raw Data Example" class="tab-image">
</div>
<div id="cfd-sample" class="tab-pane">
<img src="./assets/illustration_CFD.png" alt="Customized Figure Data Example" class="tab-image">
</div>
<div id="level2-sample" class="tab-pane">
<img src="./assets/illustration_level2.png" alt="Level 2 Example" class="tab-image">
</div>
<div id="level3-sample" class="tab-pane">
<img src="./assets/illustration_level3.png" alt="Level 3 Example" class="tab-image">
</div>
</div>
</div>
</div>
<h2 class="text-title">Overview</h2>
<p class="text-content">Chart2Code is a new benchmark for evaluating the chart understanding and code generation capabilities of large multimodal models. Chart2Code is explicitly designed from a user-driven perspective, capturing diverse real-world scenarios and progressively increasing task difficulty.</p>
<img src="./assets/figure1.png" class="responsive-image" alt="Chart2Code Overview"/>
<p class="text-content">To our knowledge, Chart2Code is the <b>first hierarchical benchmark</b> that reflects practical chart2code usage while systematically scaling task complexity. It consists of three levels as illustrated in above figure:
<br>Level1(<b>Chart Reproduction</b>) reproduces charts from a reference figure and user query;
<br>Level2(<b>Chart Editing</b>) involves complex modifications such as changing chart types or adding elements;
<br>Level3(<b>Long-Table to Chart Generation</b>) requires models to transform long, information-dense tables into faithful charts following user instructions.
</p>
<h2 class="text-title">Data Statistic</h2>
<img src="./assets/figure2.png" class="responsive-image" alt="Data Statistics"/>
<p class="text-content">In total, Chart2Code contains 2,023 tasks across 22 chart types, paired with multi-level evaluation metrics that assess both code correctness and the visual fidelity of rendered charts.</p>
<h2 class="text-title">Benchmark Comparison</h2>
<img src="./assets/figure3.png" class="responsive-image" alt="Benchmark Comparison Table"/>
<p class="text-content text-center">Table 1: Chart2Code is a unique benchmark featuring a more comprehensive set of tasks that better reflect real-world scenarios.</p>
<p class="text-content"><b>Comparison of existing chart-to-code benchmarks.</b>: Chart2Code is a new benchmark designed to rigorously evaluate chart generation capabilities of LMMs under progressively challenging conditions. This hierarchical design reflects real-world usage while progressively increasing difficulty, and its distinctions from prior benchmarks are highlighted in the above table.</p>
<h2 class="text-title">Human-Model Performance Comparison</h2>
<div class="leaderboard-container">
<div class="tabs-container">
<div class="tab-links">
<button class="tab-link active" data-tab="dr-sample">Level 1: Direct Reproduction</button>
<button class="tab-link" data-tab="crd-sample">Level 1: Custom Raw Darta</button>
<button class="tab-link" data-tab="cfd-sample">Level 1: Custom Figure Data</button>
<button class="tab-link" data-tab="level2-sample">Level 2 Example</button>
<button class="tab-link" data-tab="level3-sample">Level 3 Example</button>
</div>
<div class="tab-content">
<div id="dr-sample" class="tab-pane active">
<img src="assets/human_model_comparison_DR.png" alt="Direct Reproduction Example" class="tab-image">
</div>
<div id="crd-sample" class="tab-pane">
<img src="assets/human_model_comparison_CRD.png" alt="Customized Raw Data Example" class="tab-image">
</div>
<div id="cfd-sample" class="tab-pane">
<img src="assets/human_model_comparison_CFD.png" alt="Customized Figure Data Example" class="tab-image">
</div>
<div id="level2-sample" class="tab-pane">
<img src="assets/human_model_comparison_level2.png" alt="Level 2 Example" class="tab-image">
</div>
<div id="level3-sample" class="tab-pane">
<img src="assets/human_model_comparison_level3.png" alt="Level 3 Example" class="tab-image">
</div>
</div>
</div>
</div>
<div class="leaderboard-container">
<div class="tabs-container">
<div class="tab-links">
<button class="tab-link active" data-tab="level1">Level 1: Chart Reproduction</button>
<button class="tab-link" data-tab="level2">Level 2: Chart Editing</button>
<button class="tab-link" data-tab="level3">Level 3: Long-Table to Chart</button>
</div>
<div class="tab-content">
<div id="level1" class="tab-pane active">
<table class="sortable-table">
<thead>
<tr>
<th rowspan="2"><strong>Model</strong></th>
<th colspan="3"><strong>Direct Reproduction(DR)</strong></th>
<th colspan="3"><strong>Customize Raw Data(CRD)</strong></th>
<th colspan="3"><strong>Customize Figure Data(CFD)</strong></th>
</tr>
<tr>
<th><strong>Exec.Rate</strong></th>
<th><strong>LLM-Score</strong></th>
<th><strong>LMM-Score</strong></th>
<th><strong>Exec.Rate</strong></th>
<th><strong>LLM-Score</strong></th>
<th><strong>LMM-Score</strong></th>
<th><strong>Exec.Rate</strong></th>
<th><strong>LLM-Score</strong></th>
<th><strong>LMM-Score</strong></th>
</tr>
</thead>
<tbody>
<tr class="group-header"><td colspan="10"><em><strong>Proprietary</strong></em></td></tr>
<tr><td>Gemini-2.5-Pro</td><td>90.4</td><td>0.6286</td><td>0.3807</td><td><strong>100</strong></td><td><strong>0.6763</strong></td><td>0.2661</td><td>87.04</td><td><strong>0.6145</strong></td><td>0.2214</td></tr>
<tr><td>Claude-Sonnet-4</td><td><strong>96.38</strong></td><td>0.5629</td><td>0.2553</td><td>97.2</td><td>0.4878</td><td>0.236</td><td>88.89</td><td>0.5538</td><td>0.2273</td></tr>
<tr><td>GPT-5</td><td>87.48</td><td>0.6334</td><td>0.3575</td><td>94.4</td><td>0.6070</td><td>0.2238</td><td>85.19</td><td>0.6082</td><td>0.2382</td></tr>
<tr><td>Seed-1.5-VL</td><td>85.81</td><td>0.5536</td><td>0.2341</td><td>97.2</td><td>0.6325</td><td>0.2662</td><td>65.74</td><td>0.5756</td><td>0.1962</td></tr>
<tr><td>Seed-1.6-VL</td><td>84.70</td><td>0.5237</td><td><strong>0.2301</strong></td><td>94.4</td><td>0.6525</td><td>0.2503</td><td>83.96</td><td>0.5978</td><td>0.2075</td></tr>
<tr class="group-header"><td colspan="10"><em><strong>Open-Source LMMs (non-thinking)</strong></em></td></tr>
<tr><td>LLaVA-OV-Qwen2-7B-SI</td><td>32.82</td><td>0.1820</td><td>0.0154</td><td>11.11</td><td>0.4225</td><td>0.1550</td><td>0</td><td>-</td><td>-</td></tr>
<tr><td>LLaVA-OV-Qwen2-7B-OV</td><td>11.13</td><td>0.2651</td><td>0.0376</td><td>5.56</td><td>0.4213</td><td>0.0825</td><td>0</td><td>-</td><td>-</td></tr>
<tr><td>DeepSeek-VL-7B</td><td>48.68</td><td>0.2854</td><td>0.0431</td><td>61.11</td><td>0.5374</td><td>0.1114</td><td>10.19</td><td>0.2539</td><td>0.0145</td></tr>
<tr><td>kimi-VL-A3B</td><td>68.85</td><td>0.4409</td><td>0.1374</td><td>72.22</td><td>0.5887</td><td>0.2081</td><td>61.11</td><td>0.4641</td><td>0.1379</td></tr>
<tr><td>Qwen2-VL-7B</td><td>64.39</td><td>0.3364</td><td>0.0664</td><td>75.00</td><td>0.5950</td><td>0.1367</td><td>30.56</td><td>0.4235</td><td>0.0519</td></tr>
<tr><td>Qwen2-VL-72B</td><td>75.66</td><td>0.4368</td><td>0.1207</td><td>80.56</td><td>0.6082</td><td>0.1628</td><td>51.85</td><td>0.5518</td><td>0.1373</td></tr>
<tr><td>InternVL-2.5-8B</td><td>66.89</td><td>0.3348</td><td>0.0723</td><td>80.56</td><td>0.5712</td><td>0.1183</td><td>37.74</td><td>0.5715</td><td>0.0568</td></tr>
<tr><td>InternVL-2.5-38B</td><td>86.23</td><td>0.4577</td><td>0.1463</td><td>0</td><td>-</td><td>-</td><td>0</td><td>-</td><td>-</td></tr>
<tr><td>InternVL-3-8B</td><td>66.34</td><td>0.4371</td><td>0.1389</td><td>86.11</td><td>0.6169</td><td>0.1732</td><td>57.41</td><td>0.4450</td><td>0.1028</td></tr>
<tr><td>GLM-4V-9B</td><td>72.18</td><td>0.2881</td><td>0.0459</td><td>66.67</td><td>0.5628</td><td>0.1183</td><td>44.74</td><td>0.2904</td><td>0.0130</td></tr>
<tr><td>Intern-VL-3.5-8B</td><td>66.34</td><td>0.4371</td><td>0.1389</td><td>86.11</td><td>0.6169</td><td>0.1732</td><td>57.41</td><td>0.4450</td><td>0.1028</td></tr>
<tr><td>MiMo-VL-7B-RL</td><td>37.83</td><td>0.5439</td><td>0.2316</td><td>69.44</td><td>0.6068</td><td>0.2421</td><td>41.67</td><td>0.4962</td><td>0.1407</td></tr>
<tr><td>MiMo-VL-7B-SFT</td><td>44.65</td><td>0.4959</td><td>0.1983</td><td>69.44</td><td>0.6237</td><td>0.1852</td><td>46.30</td><td>0.5155</td><td>0.1732</td></tr>
<tr><td>Qwen2.5-VL-7B</td><td>65.64</td><td>0.4197</td><td>0.0994</td><td>75.00</td><td>0.5952</td><td>0.1515</td><td>44.44</td><td>0.5952</td><td>0.0910</td></tr>
<tr><td>Qwen2.5-VL-72B</td><td>65.36</td><td>0.5118</td><td>0.1893</td><td>100</td><td>0.6273</td><td>0.1989</td><td>37.96</td><td>0.5532</td><td>0.1688</td></tr>
<tr><td>Molmo-7B-D</td><td>34.77</td><td>0.2164</td><td>0.0943</td><td>4.55</td><td>0.2400</td><td><strong>0.4600</strong></td><td>0.97</td><td>0.0500</td><td><strong>0.4100</strong></td></tr>
<tr><td>Qwen3-30B</td><td>64.67</td><td>0.5293</td><td>0.2531</td><td>77.78</td><td>0.2546</td><td>0.2368</td><td>70.37</td><td>0.2412</td><td>0.2698</td></tr>
<tr class="group-header"><td colspan="10"><em><strong>Open-Source LMMs (thinking)</strong></em></td></tr>
<tr><td>MiMo-VL-7B-RL</td><td>55.77</td><td>0.5261</td><td>0.2294</td><td>69.44</td><td>0.6053</td><td>0.2582</td><td>33.33</td><td>0.5807</td><td>0.2172</td></tr>
<tr><td>MiMo-VL-7B-SFT</td><td>50.35</td><td><strong>0.6555</strong></td><td>0.2130</td><td>86.11</td><td>0.6644</td><td>0.2248</td><td>38.89</td><td>0.5578</td><td>0.1455</td></tr>
<tr><td>Qwen3-30B</td><td>45.06</td><td>0.5582</td><td>0.2730</td><td>72.22</td><td>0.3367</td><td>0.3368</td><td>39.81</td><td>0.3185</td><td>0.2780</td></tr>
</tbody>
</table>
</div>
<div id="level2" class="tab-pane">
<table class="sortable-table">
<thead>
<tr>
<th rowspan="2"><strong>Model</strong></th>
<th rowspan="2"><strong>Exec.<br>Rate</strong></th>
<th colspan="9"><strong>Code-Level</strong></th>
<th rowspan="2"><strong>Chart-Level<br>LMM-Score</strong></th>
</tr>
<tr>
<th><strong>Color</strong></th><th><strong>Grid</strong></th><th><strong>Layout</strong></th><th><strong>Legend</strong></th><th><strong>Visual</strong></th><th><strong>Data</strong></th><th><strong>Text</strong></th><th><strong>Type</strong></th><th><strong>LLM-Score</strong></th>
</tr>
</thead>
<tbody>
<tr class="group-header"><td colspan="12"><em><strong>Proprietary</strong></em></td></tr>
<tr><td>Gemini-2.5-Pro</td><td>90.30</td><td><strong>0.6217</strong></td><td><strong>0.8842</strong></td><td><strong>0.9613</strong></td><td><strong>0.5093</strong></td><td><strong>0.5170</strong></td><td><strong>0.7560</strong></td><td>0.6330</td><td><strong>0.9636</strong></td><td><strong>0.5742</strong></td><td>0.2459</td></tr>
<tr><td>Claude-Sonnet-4</td><td><strong>91.19</strong></td><td>0.5737</td><td>0.8110</td><td>0.9587</td><td>0.4714</td><td>0.4776</td><td>0.6736</td><td>0.5869</td><td>0.9563</td><td>0.5317</td><td>0.2147</td></tr>
<tr><td>GPT-5</td><td>90.58</td><td>0.5812</td><td>0.8467</td><td>0.9499</td><td>0.4835</td><td>0.4815</td><td>0.7047</td><td>0.6096</td><td>0.9581</td><td>0.5663</td><td><strong>0.2506</strong></td></tr>
<tr><td>Seed-1.5-VL</td><td>63.17</td><td>0.5106</td><td>0.8230</td><td>0.9538</td><td>0.4408</td><td>0.4582</td><td>0.6983</td><td>0.7166</td><td>0.9400</td><td>0.5126</td><td>0.1975</td></tr>
<tr><td>Seed-1.6-VL</td><td>72.38</td><td>0.5277</td><td>0.8013</td><td>0.9471</td><td>0.4714</td><td>0.4453</td><td>0.6884</td><td><strong>0.7312</strong></td><td>0.9431</td><td>0.5151</td><td>0.1863</td></tr>
<tr class="group-header"><td colspan="12"><em><strong>Open-Source LMMs (non-thinking)</strong></em></td></tr>
<tr><td>LLaVA-OV-Qwen2-7B-SI</td><td>1.19</td><td>0.3507</td><td>0.6964</td><td>0.7833</td><td>0.4074</td><td>0.3002</td><td>0.5249</td><td>0.4871</td><td>0.7889</td><td>0.3157</td><td>0.0875</td></tr>
<tr><td>LLaVA-OV-Qwen2-7B-OV</td><td>2.57</td><td>0.3163</td><td>0.6013</td><td>0.6863</td><td>0.4488</td><td>0.2030</td><td>0.5685</td><td>0.4928</td><td>0.8154</td><td>0.3512</td><td>0.0366</td></tr>
<tr><td>DeepSeek-VL-7B</td><td>21.68</td><td>0.2523</td><td>0.6206</td><td>0.7350</td><td>0.2436</td><td>0.1820</td><td>0.4031</td><td>0.4538</td><td>0.7922</td><td>0.2583</td><td>0.0433</td></tr>
<tr><td>kimi-VL-A3B</td><td>49.5</td><td>0.3901</td><td>0.7270</td><td>0.9074</td><td>0.3411</td><td>0.3196</td><td>0.5724</td><td>0.5913</td><td>0.9033</td><td>0.3701</td><td>0.1039</td></tr>
<tr><td>Qwen2-VL-7B</td><td>24.95</td><td>0.2846</td><td>0.5825</td><td>0.7711</td><td>0.2723</td><td>0.2385</td><td>0.4693</td><td>0.4883</td><td>0.8141</td><td>0.3181</td><td>0.0780</td></tr>
<tr><td>Qwen2-VL-72B</td><td>55.05</td><td>0.4013</td><td>0.7704</td><td>0.9044</td><td>0.3464</td><td>0.3345</td><td>0.6086</td><td>0.5744</td><td>0.9098</td><td>0.3928</td><td>0.1140</td></tr>
<tr><td>InternVL-2.5-8B</td><td>21.29</td><td>0.3341</td><td>0.7002</td><td>0.8362</td><td>0.3148</td><td>0.2955</td><td>0.5421</td><td>0.5530</td><td>0.8536</td><td>0.3344</td><td>0.0869</td></tr>
<tr><td>InternVL-2.5-38B</td><td>68.22</td><td>0.4544</td><td>0.7902</td><td>0.9405</td><td>0.4146</td><td>0.3745</td><td>0.6334</td><td>0.6361</td><td>0.9338</td><td>0.4311</td><td>0.1367</td></tr>
<tr><td>InternVL-3-8B</td><td>4.55</td><td>0.3491</td><td>0.5914</td><td>0.9447</td><td>0.3389</td><td>0.3645</td><td>0.5561</td><td>0.5421</td><td>0.8556</td><td>0.3419</td><td>0.0943</td></tr>
<tr><td>InternVL-3-38B</td><td>67.43</td><td>0.4720</td><td>0.7853</td><td>0.9410</td><td>0.4133</td><td>0.3994</td><td>0.6525</td><td>0.6538</td><td>0.9235</td><td>0.4528</td><td>0.1476</td></tr>
<tr><td>GLM-4V-9B</td><td>10.69</td><td>0.2011</td><td>0.6910</td><td>0.7794</td><td>0.2357</td><td>0.2196</td><td>0.4604</td><td>0.5003</td><td>0.7472</td><td>0.2953</td><td>0.0770</td></tr>
<tr><td>Intern-VL-3.5-8B</td><td>27.23</td><td>0.4015</td><td>0.7350</td><td>0.9056</td><td>0.3566</td><td>0.3718</td><td>0.6121</td><td>0.6505</td><td>0.8998</td><td>0.3964</td><td>0.1466</td></tr>
<tr><td>MiMo-VL-7B-RL</td><td>20.59</td><td>0.4378</td><td>0.8462</td><td>0.9205</td><td>0.4201</td><td>0.4231</td><td>0.6505</td><td>0.6666</td><td>0.9200</td><td>0.4615</td><td>0.1573</td></tr>
<tr><td>MiMo-VL-7B-SFT</td><td>21.88</td><td>0.4325</td><td>0.7506</td><td>0.8941</td><td>0.3823</td><td>0.4035</td><td>0.6431</td><td>0.6564</td><td>0.9405</td><td>0.4459</td><td>0.1399</td></tr>
<tr><td>Qwen2.5-VL-7B</td><td>33.36</td><td>0.3524</td><td>0.7374</td><td>0.8592</td><td>0.3296</td><td>0.3302</td><td>0.5944</td><td>0.5780</td><td>0.8887</td><td>0.3603</td><td>0.0974</td></tr>
<tr><td>Qwen2.5-VL-72B</td><td>71.49</td><td>0.5018</td><td>0.8229</td><td>0.9509</td><td>0.4467</td><td>0.4242</td><td>0.6673</td><td>0.6815</td><td>0.9348</td><td>0.4739</td><td>0.1684</td></tr>
<tr><td>Molmo-7B-D</td><td>0.99</td><td>0.2471</td><td>0.8152</td><td>0.5636</td><td>0.1000</td><td>0.2275</td><td>0.3477</td><td>0.3082</td><td>0.3476</td><td>0.3488</td><td>0.1347</td></tr>
<tr><td>Qwen3-30B</td><td>41.39</td><td>0.54</td><td>0.8174</td><td>0.9587</td><td>0.4623</td><td>0.4501</td><td>0.6911</td><td>0.7084</td><td>0.9384</td><td>0.3611</td><td>0.2257</td></tr>
<tr class="group-header"><td colspan="12"><em><strong>Open-Source LMMs (thinking)</strong></em></td></tr>
<tr><td>MiMo-VL-7B-RL</td><td>27.62</td><td>0.5076</td><td>0.7560</td><td>0.9449</td><td>0.4109</td><td>0.4379</td><td>0.7006</td><td>0.6859</td><td>0.9446</td><td>0.4819</td><td>0.1737</td></tr>
<tr><td>MiMo-VL-7B-SFT</td><td>24.16</td><td>0.4562</td><td>0.7404</td><td>0.9286</td><td>0.3686</td><td>0.3980</td><td>0.6812</td><td>0.6617</td><td>0.9385</td><td>0.4496</td><td>0.1774</td></tr>
<tr><td>Qwen3-30B</td><td>42.38</td><td>0.5213</td><td>0.8248</td><td>0.9549</td><td>0.4718</td><td>0.4453</td><td>0.6924</td><td>0.7046</td><td>0.9403</td><td>0.4947</td><td>0.2362</td></tr>
</tbody>
</table>
</div>
<div id="level3" class="tab-pane">
<table class="sortable-table">
<thead>
<tr>
<th rowspan="2"><strong>Model</strong></th>
<th rowspan="2"><strong>Exec.<br>Rate</strong></th>
<th colspan="9"><strong>Code-Level</strong></th>
<th rowspan="2"><strong>Figure-Level<br>LMM-Score</strong></th>
</tr>
<tr>
<th><strong>Color</strong></th><th><strong>Grid</strong></th><th><strong>Layout</strong></th><th><strong>Legend</strong></th><th><strong>Visual</strong></th><th><strong>Data</strong></th><th><strong>Text</strong></th><th><strong>Type</strong></th><th><strong>LLM-Score</strong></th>
</tr>
</thead>
<tbody>
<tr class="group-header"><td colspan="12"><em><strong>Proprietary</strong></em></td></tr>
<tr><td>Gemini-2.5-Pro</td><td>29.33</td><td><strong>0.7276</strong></td><td><strong>0.9733</strong></td><td>1.0000</td><td>0.7727</td><td><strong>0.6701</strong></td><td>0.7880</td><td><strong>0.8291</strong></td><td>0.9470</td><td>0.3516</td><td>0.0361</td></tr>
<tr><td>Claude-Sonnet-4</td><td><strong>38.00</strong></td><td>0.5676</td><td>0.7963</td><td>1.0000</td><td>0.8148</td><td>0.3731</td><td>0.5881</td><td>0.7175</td><td>0.9062</td><td><strong>0.5125</strong></td><td>0.007</td></tr>
<tr><td>GPT-5</td><td><strong>38.00</strong></td><td>0.5676</td><td>0.7963</td><td>1.0000</td><td>0.8148</td><td>0.3731</td><td>0.5881</td><td>0.7175</td><td>0.9062</td><td><strong>0.5125</strong></td><td>0.0362</td></tr>
<tr><td>Seed-1.5-VL</td><td>18.67</td><td>0.7252</td><td>0.8929</td><td>1.0000</td><td><strong>0.8869</strong></td><td>0.5502</td><td>0.7182</td><td>0.7804</td><td><strong>0.9690</strong></td><td>0.0000</td><td><strong>0.0611</strong></td></tr>
<tr><td>Seed-1.6-VL</td><td>40.00</td><td>0.7030</td><td>0.8833</td><td>1.0000</td><td>0.7972</td><td>0.5396</td><td><strong>0.7956</strong></td><td>0.8128</td><td>0.9244</td><td>0.0000</td><td>0.0547</td></tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
<h2 class="text-title">Results Analysis</h2>
<img src="assets/figure7.png" class="responsive-image" alt="Correlation of model performance">
<p class="text-content text-center">Correlation of the model performance (i.e., LMM-score) on different manually annotated difficulties (i.e., Easy, Medium, Hard) on Level 1, 2, 3, respectively.</p>
<img src="assets/figure8.png" class="responsive-image" alt="Model generalization analysis">
<p class="text-content">Left: Both proprietary and open-source models generalize well on Level 1 and Level 2 tasks when calculating the LLM-score for predicted code assessment. Right: Proprietary models tend to obtain higher LMM-scores on the Level 1 task rather than the Level 2, while open-source models perform poorly on both tasks (scores are lower than 0.5).</p>
<img src="assets/figure9.png" class="responsive-image" alt="Performance analysis on different task cases">
<p class="text-content text-center">Analysis of model performance on different task cases with LLM-score and LMM-score.</p>
<h2 class="text-title">Example of Data and Error Cases</h2>
<div class="leaderboard-container">
<div class="tabs-container">
<div class="tab-links">
<button class="tab-link active" data-tab="dr-sample">Level 1: Direct Reproduction</button>
<button class="tab-link" data-tab="crd-sample">Level 1: Custom Raw Darta</button>
<button class="tab-link" data-tab="cfd-sample">Level 1: Custom Figure</button>
<button class="tab-link" data-tab="level2-sample">Level 2 Example</button>
<button class="tab-link" data-tab="level3-sample">Level 3 Example</button>
<button class="tab-link" data-tab="error-cases">Error Cases</button>
</div>
<div class="tab-content">
<div id="dr-sample" class="tab-pane active">
<h3 class="text-title">An Example of Level 1: Direct Reproduction</h3>
<img src="assets/DR_sample.png" alt="Direct Reproduction Example" class="tab-image">
</div>
<div id="crd-sample" class="tab-pane">
<h3 class="text-title">An Example of Level 1: Customized Text-Format Table Data</h3>
<img src="assets/CRD_sample.png" alt="Customized Raw Data Example" class="tab-image">
</div>
<div id="cfd-sample" class="tab-pane">
<h3 class="text-title">An Example of Level 1: Figure-Format Table Data</h3>
<img src="assets/CFD_sample.png" alt="Customized Figure Data Example" class="tab-image">
</div>
<div id="level2-sample" class="tab-pane">
<h3 class="text-title">An Example of Level 2</h3>
<img src="assets/level2_sample.png" alt="Level 2 Example" class="tab-image">
</div>
<div id="level3-sample" class="tab-pane">
<h3 class="text-title">An Example of Level 3</h3>
<img src="assets/level3_sample.png" alt="Level 3 Example" class="tab-image">
</div>
<div id="error-cases" class="tab-pane">
<h3 class="text-title">Error Cases Visualization</h3>
<img src="assets/error_analyse.png" alt="Error Analysis 1" class="tab-image" style="margin-bottom: 20px;">
<img src="assets/error_analyse_2.png" alt="Error Analysis 2" class="tab-image">
</div>
</div>
</div>
</div>
<section id="failure-case-analysis" class="failure-case-section">
<div id="failure-case-gallery"></div>
</section>
<h2 class="text-title">Citation</h2>
<pre id="citation"><code>
@misc{tang2025chartscodehierarchicalbenchmark,
title={From Charts to Code: A Hierarchical Benchmark for Multimodal Models},
author={Jiahao Tang and Henry Hengyuan Zhao and Lijian Wu and Yifei Tao and Dongxing Mao and Yang Wan and Jingru Tan and Min Zeng and Min Li and Alex Jinpeng Wang},
year={2025},
eprint={2510.17932},
archivePrefix={arXiv},
primaryClass={cs.SE},
url={https://arxiv.org/abs/2510.17932},
}
</code></pre>
<p class="acknowledgements">
<b>Acknowledge:</b> Thanks to Carlos & John for this webpage template. Also thanks to the SWE-bench team and their benchmark <a href="https://www.swebench.com/multimodal.html">https://www.swebench.com/multimodal.html</a>.
</p>
<p class="acknowledgements">
<b>Template Usage:</b> If you would like to use this website template for your own leaderboard, please <span style="color:brown">send Carlos & John an email requesting permission.</span> If granted, please make sure to acknowledge the SWE-bench team and link to this leaderboard on the home page of the website.
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