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<h1 class="title is-1 publication-title">🏟️ OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI</h1>
<div class="is-size-5 publication-authors">
<span class="author-block"><a href="http://huangzhen02.github.io">Zhen Huang</a><sup>3</sup>,</span>
<span class="author-block"><a href="https://tinyurl.com/zengzhi-homepage">Zengzhi Wang</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="https://shijie-xia.github.io/">Shijie Xia</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="https://github.com/hongtangshui">Xuefeng Li</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="https://github.com/haoy-zzz">Haoyang Zou</a><sup>3</sup>,</span>
<span class="author-block"><a href="https://github.com/VagueWine">Ruijie Xu</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="https://rzfan525.github.io/">Run-Ze Fan</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="https://greenyea-com.webflow.io/">Lyumanshan Ye</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="https://ethanc111.github.io/">Ethan Chern</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="https://github.com/BLeaves">Yixin Ye</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="http://arist12.github.io/ykzhang/">Yikai Zhang</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="https://ayyyq.github.io/">Yuqing Yang</a><sup>3</sup>,</span>
<span class="author-block"><a href="http://cuteythyme.github.io">Ting Wu</a><sup>3</sup>,</span>
<span class="author-block"><a href="https://www.linkedin.com/in/binjie-wang-91514b25a/">Binjie Wang</a><sup>3</sup>,</span>
<span class="author-block"><a href="https://shichaosun.github.io/">Shichao Sun</a><sup>3</sup>,</span>
<span class="author-block"><a href="https://xiaoyang66.github.io/">Yang Xiao</a><sup>3</sup>,</span>
<span class="author-block"><a href="https://nativeatom.github.io/">Yiyuan Li</a><sup>3</sup>,</span>
<span class="author-block"><a href="https://koalazf99.github.io/">Fan Zhou</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="https://steffichern.github.io/">Steffi Chern</a><sup>3</sup>,</span>
<span class="author-block"><a href="https://qinyiwei.github.io/">Yiwei Qin</a><sup>3</sup>,</span>
<span class="author-block"><a href="https://mantle2048.github.io/">Yan Ma</a><sup>3</sup>,</span>
<span class="author-block"><a href="https://github.com/JoyBoy-Su">Jiadi Su</a><sup>3</sup>,</span>
<span class="author-block"><a href="https://github.com/yxliu0903">Yixiu Liu</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="https://github.com/zeetc">Yuxiang Zheng</a><sup>1, 3</sup>,</span>
<span class="author-block"><a href="https://scholar.google.com/citations?user=oiBMWK4AAAAJ&hl=en">Shaoting Zhang</a><sup>2*</sup>,</span>
<span class="author-block"><a href="http://dahua.site/">Dahua Lin</a><sup>2*</sup>,</span>
<span class="author-block"><a href="http://mmlab.siat.ac.cn/yuqiao">Yu Qiao</a><sup>2*</sup>,</span>
<span class="author-block"><a href="http://pfliu.com/">Pengfei Liu</a><sup>1, 2, 3*</sup></span>
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<span class="author-block"><sup>1</sup>Shanghai Jiao Tong University,</span>
<span class="author-block"><sup>2</sup>Shanghai Artificial Intelligence Laboratory,</span>
<span class="author-block"><sup>3</sup>Generative AI Research Lab (GAIR)</span>
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<span class="author-block"><sup>*</sup>Corresponding authors</span>
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<p>
The evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showcasing potential cognitive reasoning abilities in problem-solving and scientific discovery (i.e., AI4Science) once exclusive to human intellect. In this paper, we introduce OlympicArena, a comprehensive, highly-challenging, and rigorously curated benchmark featuring a detailed, fine-grained evaluation mechanism designed to assess advanced AI capabilities across a broad spectrum of Olympic-level challenges.
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<ul>
<li><b>Comprehensive: </b>The benchmark includes a comprehensive set of 11,163 problems from 62 distinct Olympic competitions, structured with 13 answer types. It spans seven core disciplines: <b>mathematics, physics, chemistry, biology, geography, astronomy, and computer science</b>, encompassing a total of 34 specialized branches</li>
<li><b>High-challenging: </b>The benchmark focuses on Olympic-level problems and covers 8 types of logical reasoning abilities and 5 types of visual reasoning abilities.</li>
<li><b>Rigorous: </b>Given the increasing scale of pre-training corpora, it is crucial to detect potential benchmark leakage. We employ a recently proposed instance-level leakage detection metric to validate our benchmark’s effectiveness.</li>
<li><b>Fine-grained Evaluation: </b>We conduct comprehensive evaluations from both the answer-level and process-level perspectives. Additionally, we perform fine-grained evaluations and analyses on different types of cognitive reasoning, from both logical and visual perspectives to better interpret the current capabilities of AI.</li>
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<p>
Through the OlympicArena, we aim to advance AI towards superintelligence, equipping it to address more complex challenges in science and beyond.
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We evaluate various models, including LLMs and LMMs, considering both closed- and open-source versions. All experiments utilize zero-shot prompts, specifically tailored to each answer type and specifying output formats to facilitate answer extraction and rule-based matching. For the LLMs, we do not provide any image information, considering the potential hallucinations and instability in captions, which also allows for a better comparison with LMMs.
</p>
<p>
For CS problems, we set the inference temperature to 0.2 to obtain multiple and diverse candidate results, while for all other disciplines, the temperature is set to 0.0. Additionally, we set the maximum length of output tokens to 2048. Note that some LMMs only support a single image input; in such cases, we input only the first image. For LMMs that require a compulsive image input, we use their corresponding text-only model when handling text-only problems. Here is the leaderboard for our benchmark's validation and test sets.
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Note: The results reported in the paper are based on the combined outcomes from the validation and test sets, as well as some additional problems evaluated by the models. Therefore, they may slightly differ from the data presented below.
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<span class="leaderboard-label" style="background-color: rgba(249, 242, 248, 1);">Open-Source</span>
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<td class="js-sort-number" style="min-width: 160px;"><strong>Model</strong></td>
<td class="js-sort-string"><strong>Setting</strong></td>
<td class="js-sort-date" style="min-width: 100px;"><strong>Date</strong></td>
<td class="js-sort-number"><strong>Overall</strong></td>
<td class="js-sort-number"><strong>Math</strong></td>
<td class="js-sort-number"><strong>Physics</strong></td>
<td class="js-sort-number"><strong>Chemistry</strong></td>
<td class="js-sort-number"><strong>Biology</strong></td>
<td class="js-sort-number"><strong>Geography</strong></td>
<td class="js-sort-number"><strong>Astronomy</strong></td>
<td class="js-sort-number"><strong>CS</strong></td>
<td class="js-sort-number"><strong>EN</strong></td>
<td class="js-sort-number"><strong>ZH</strong></td>
<td class="js-sort-number" style="min-width: 100px;"><strong>Text-only</strong></td>
<td class="js-sort-number" style="min-width: 110px;"><strong>Multi-modal</strong></td>
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<a href="https://openai.com/index/hello-gpt-4o/">
<b>GPT-4o</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-12</td>
<td>34.01</td>
<td>34.43</td>
<td>34.44</td>
<td>29.23</td>
<td>30.16</td>
<td>44.12</td>
<td>33.33</td>
<td>15.56</td>
<td>34.44</td>
<td>33.55</td>
<td>35.93</td>
<td>31.54</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://openai.com/index/gpt-4v-system-card/">
<b>GPT-4V</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-12</td>
<td>26.18</td>
<td>22.95</td>
<td>26.67</td>
<td>30.77</td>
<td>25.40</td>
<td>33.82</td>
<td>28.89</td>
<td>7.78</td>
<td>24.47</td>
<td>28.01</td>
<td>27.30</td>
<td>24.73</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://www.anthropic.com/claude">
<b>Claude-3-Sonnet</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-12</td>
<td>18.97</td>
<td>12.70</td>
<td>20.00</td>
<td>18.46</td>
<td>28.57</td>
<td>25.00</td>
<td>25.56</td>
<td>6.67</td>
<td>19.34</td>
<td>18.57</td>
<td>18.66</td>
<td>19.35</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://deepmind.google/technologies/gemini/pro/">
<b>Gemini Pro Vision</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-12</td>
<td>17.08</td>
<td>11.07</td>
<td>15.56</td>
<td>15.38</td>
<td>33.33</td>
<td>30.88</td>
<td>16.67</td>
<td>2.22</td>
<td>12.69</td>
<td>21.82</td>
<td>16.43</td>
<td>17.92</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://huggingface.co/spaces/Qwen/Qwen-VL-Max">
<b>Qwen-VL-Max</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-12</td>
<td>17.08</td>
<td>10.25</td>
<td>10.00</td>
<td>20.00</td>
<td>28.57</td>
<td>35.29</td>
<td>22.22</td>
<td>0</td>
<td>11.18</td>
<td>23.45</td>
<td>16.43</td>
<td>17.92</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/liuhaotian/llava-v1.6-34b">
<b>LLaVA-NeXT-34B</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-12</td>
<td>12.70</td>
<td>3.69</td>
<td>8.89</td>
<td>15.38</td>
<td>26.98</td>
<td>35.29</td>
<td>14.44</td>
<td>0</td>
<td>6.34</td>
<td>19.54</td>
<td>8.91</td>
<td>17.56</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5">
<b>InternVL-Chat-V1.5</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-12</td>
<td>12.70</td>
<td>5.74</td>
<td>7.78</td>
<td>15.38</td>
<td>28.57</td>
<td>27.94</td>
<td>13.33</td>
<td>2.78</td>
<td>7.85</td>
<td>17.92</td>
<td>11.70</td>
<td>13.98</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/01-ai/Yi-VL-34B">
<b>Yi-VL-34B</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-12</td>
<td>9.87</td>
<td>3.28</td>
<td>6.67</td>
<td>13.85</td>
<td>19.05</td>
<td>25.00</td>
<td>12.22</td>
<td>0</td>
<td>5.44</td>
<td>14.66</td>
<td>9.19</td>
<td>10.75</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/Qwen/Qwen-VL-Chat">
<b>Qwen-VL-Chat</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-12</td>
<td>4.08</td>
<td>2.05</td>
<td>5.56</td>
<td>4.62</td>
<td>4.76</td>
<td>7.35</td>
<td>5.56</td>
<td>0</td>
<td>3.63</td>
<td>4.56</td>
<td>4.18</td>
<td>3.94</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/Qwen/Qwen1.5-32B-Chat">
<b>Qwen1.5-32B-Chat</b>
</a>
</td>
<td>LLM</td>
<td>2024-6-20</td>
<td>21.16</td>
<td>13.11</td>
<td>15.56</td>
<td>16.92</td>
<td>28.57</td>
<td>47.06</td>
<td>30.00</td>
<td>3.33</td>
<td>15.11</td>
<td>27.69</td>
<td>19.22</td>
<td>23.66</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/internlm/internlm2-chat-20b">
<b>Internlm2-Chat-20B</b>
</a>
</td>
<td>LLM</td>
<td>2024-6-20</td>
<td>13.95</td>
<td>7.38</td>
<td>7.78</td>
<td>18.46</td>
<td>31.75</td>
<td>32.35</td>
<td>10.00</td>
<td>2.22</td>
<td>8.16</td>
<td>20.20</td>
<td>11.70</td>
<td>16.85</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/01-ai/Yi-34B-Chat">
<b>Yi-34B-Chat</b>
</a>
</td>
<td>LLM</td>
<td>2024-6-20</td>
<td>12.54</td>
<td>3.28</td>
<td>7.78</td>
<td>15.38</td>
<td>20.63</td>
<td>42.65</td>
<td>14.44</td>
<td>0</td>
<td>5.44</td>
<td>20.20</td>
<td>8.64</td>
<td>17.56</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/Qwen/Qwen-7B-Chat">
<b>Qwen-7B-Chat</b>
</a>
</td>
<td>LLM</td>
<td>2024-6-20</td>
<td>3.45</td>
<td>2.05</td>
<td>3.33</td>
<td>3.08</td>
<td>7.94</td>
<td>2.94</td>
<td>5.56</td>
<td>0</td>
<td>2.11</td>
<td>4.89</td>
<td>3.34</td>
<td>3.58</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct">
<b>Llama-3-70B-Instruct</b>
</a>
</td>
<td>LLM</td>
<td>2024-6-21</td>
<td>21.63</td>
<td>15.16</td>
<td>22.22</td>
<td>21.54</td>
<td>26.98</td>
<td>32.35</td>
<td>30.00</td>
<td>2.22</td>
<td>19.03</td>
<td>24.43</td>
<td>22.56</td>
<td>20.43</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://www.anthropic.com/news/claude-3-5-sonnet">
<b>Claude-3.5-Sonnet</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-21</td>
<td>33.54</td>
<td>30.74</td>
<td>31.11</td>
<td>30.77</td>
<td>39.68</td>
<td>45.59</td>
<td>35.56</td>
<td>14.44</td>
<td>30.21</td>
<td>37.13</td>
<td>35.38</td>
<td>31.18</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://ai.google.dev/gemini-api">
<b>Gemini-1.5-Pro</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-22</td>
<td>28.68</td>
<td>23.77</td>
<td>27.78</td>
<td>26.15</td>
<td>31.75</td>
<td>44.12</td>
<td>33.33</td>
<td>12.22</td>
<td>25.08</td>
<td>32.57</td>
<td>28.69</td>
<td>28.67</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://www.volcengine.com/product/doubao">
<b>Doubao-Pro-32k</b>
</a>
</td>
<td>LLM</td>
<td>2024-6-25</td>
<td>31.66</td>
<td>25.82</td>
<td>22.22</td>
<td>36.92</td>
<td>50.79</td>
<td>41.18</td>
<td>36.67</td>
<td>8.89</td>
<td>23.26</td>
<td>40.72</td>
<td>33.43</td>
<td>29.39</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://github.com/deepseek-ai/DeepSeek-Coder-V2">
<b>DeepSeek-Coder-V2</b>
</a>
</td>
<td>LLM</td>
<td>2024-6-26</td>
<td>29.31</td>
<td>29.51</td>
<td>26.67</td>
<td>23.08</td>
<td>33.33</td>
<td>35.29</td>
<td>32.22</td>
<td>6.67</td>
<td>27.79</td>
<td>30.94</td>
<td>32.03</td>
<td>25.81</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/Qwen/Qwen2-72B-Instruct">
<b>Qwen2-72B-Instruct</b>
</a>
</td>
<td>LLM</td>
<td>2024-7-5</td>
<td>28.84</td>
<td>26.23</td>
<td>22.22</td>
<td>18.46</td>
<td>38.10</td>
<td>47.06</td>
<td>34.44</td>
<td>5.56</td>
<td>23.87</td>
<td>34.20</td>
<td>30.92</td>
<td>26.16</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/internlm/internlm2_5-7b-chat">
<b>Internlm-2.5-7B-Chat</b>
</a>
</td>
<td>LLM</td>
<td>2024-7-8</td>
<td>18.65</td>
<td>13.52</td>
<td>11.11</td>
<td>12.31</td>
<td>34.92</td>
<td>36.92</td>
<td>22.22</td>
<td>5.56</td>
<td>14.20</td>
<td>23.45</td>
<td>17.55</td>
<td>20.07</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5">
<b>MiniCPM-Llama3-V-2_5</b>
</a>
</td>
<td>LMM</td>
<td>2024-7-12</td>
<td>11.91</td>
<td>5.33</td>
<td>13.33</td>
<td>10.77</td>
<td>23.81</td>
<td>23.53</td>
<td>13.33</td>
<td>2.22</td>
<td>8.76</td>
<td>15.31</td>
<td>11.14</td>
<td>12.90</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/mistralai/mathstral-7B-v0.1">
<b>Mathstral-7B-v0.1</b>
</a>
</td>
<td>LLM</td>
<td>2024-7-22</td>
<td>16.61</td>
<td>15.98</td>
<td>15.56</td>
<td>10.77</td>
<td>15.87</td>
<td>25.00</td>
<td>20.00</td>
<td>4.44</td>
<td>14.50</td>
<td>18.89</td>
<td>17.83</td>
<td>15.05</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/OpenGVLab/InternVL2-8B">
<b>InternVL2-8B</b>
</a>
</td>
<td>LMM</td>
<td>2024-7-22</td>
<td>14.73</td>
<td>12.70</td>
<td>7.78</td>
<td>10.77</td>
<td>23.81</td>
<td>29.41</td>
<td>15.56</td>
<td>0.00</td>
<td>10.57</td>
<td>19.22</td>
<td>15.60</td>
<td>13.62</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/">
<b>GPT-4o-mini</b>
</a>
</td>
<td>LMM</td>
<td>2024-7-24</td>
<td>28.37</td>
<td>34.02</td>
<td>25.56</td>
<td>16.92</td>
<td>31.75</td>
<td>30.88</td>
<td>23.33</td>
<td>6.67</td>
<td>28.70</td>
<td>28.01</td>
<td>32.87</td>
<td>22.58</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/internlm/internlm-xcomposer2d5-7b">
<b>Internlm-xcomposer-2.5-7B</b>
</a>
</td>
<td>LMM</td>
<td>2024-7-24</td>
<td>12.70</td>
<td>5.74</td>
<td>6.67</td>
<td>18.46</td>
<td>22.22</td>
<td>30.88</td>
<td>15.56</td>
<td>0.00</td>
<td>7.25</td>
<td>18.57</td>
<td>10.86</td>
<td>15.05</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct">
<b>Llama-3.1-8B-Instruct</b>
</a>
</td>
<td>LLM</td>
<td>2024-8-1</td>
<td>14.73</td>
<td>11.48</td>
<td>17.78</td>
<td>10.77</td>
<td>19.05</td>
<td>27.94</td>
<td>12.22</td>
<td>5.56</td>
<td>14.50</td>
<td>14.98</td>
<td>15.32</td>
<td>13.98</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/OpenGVLab/InternVL2-40B">
<b>InternVL2-40B</b>
</a>
</td>
<td>LMM</td>
<td>2024-8-6</td>
<td>15.52</td>
<td>5.74</td>
<td>14.44</td>
<td>10.77</td>
<td>38.10</td>
<td>36.76</td>
<td>17.78</td>
<td>0.00</td>
<td>11.48</td>
<td>19.87</td>
<td>14.21</td>
<td>17.20</td>
</tr>
<tr style="background-color: rgba(249, 242, 248, 1);">
<td style="text-align: left;">
<a href="https://huggingface.co/google/gemma-2-27b-it">
<b>Gemma2-27B-it</b>
</a>
</td>
<td>LLM</td>
<td>2024-8-15</td>
<td>19.91</td>
<td>14.34</td>
<td>23.33</td>
<td>18.46</td>
<td>28.57</td>
<td>29.41</td>
<td>22.22</td>
<td>1.11</td>
<td>17.52</td>
<td>22.48</td>
<td>19.78</td>
<td>20.07</td>
</tr>
</table>
<!-- Test Set Leaderboard -->
<table id="table2" class="js-sort-table table hidden">
<tr>
<td class="js-sort-number" style="min-width: 160px;"><strong>Model</strong></td>
<td class="js-sort-string"><strong>Setting</strong></td>
<td class="js-sort-date" style="min-width: 100px;"><strong>Date</strong></td>
<td class="js-sort-number"><strong>Overall</strong></td>
<td class="js-sort-number"><strong>Math</strong></td>
<td class="js-sort-number"><strong>Physics</strong></td>
<td class="js-sort-number"><strong>Chemistry</strong></td>
<td class="js-sort-number"><strong>Biology</strong></td>
<td class="js-sort-number"><strong>Geography</strong></td>
<td class="js-sort-number"><strong>Astronomy</strong></td>
<td class="js-sort-number"><strong>CS</strong></td>
<td class="js-sort-number"><strong>EN</strong></td>
<td class="js-sort-number"><strong>ZH</strong></td>
<td class="js-sort-number" style="min-width: 100px;"><strong>Text-only</strong></td>
<td class="js-sort-number" style="min-width: 110px;"><strong>Multi-modal</strong></td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://openai.com/index/hello-gpt-4o/">
<b>GPT-4o</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-12</td>
<td>40.47</td>
<td>28.32</td>
<td>30.01</td>
<td>46.68</td>
<td>53.11</td>
<td>56.77</td>
<td>44.50</td>
<td>8.43</td>
<td>44.16</td>
<td>34.59</td>
<td>41.79</td>
<td>39.03</td>
</tr>
<tr style="background-color: rgba(117, 209, 215, 0.1);">
<td style="text-align: left;">
<a href="https://openai.com/index/gpt-4v-system-card/">
<b>GPT-4V</b>
</a>
</td>
<td>LMM</td>
<td>2024-6-12</td>
<td>33.17</td>
<td>18.98</td>
<td>24.94</td>
<td>41.06</td>
<td>47.69</td>
<td>50.33</td>
<td>32.07</td>
<td>6.94</td>
<td>37.17</td>
<td>26.49</td>