{"version":"5.49.1","api_prefix":"/gradio_api","mode":"blocks","app_id":6248500800526428139,"dev_mode":false,"vibe_mode":false,"analytics_enabled":true,"components":[{"id":1,"type":"html","props":{"value":"<h1 align=\"center\" id=\"space-title\">GAIA Leaderboard</h1>","show_label":false,"visible":true,"elem_classes":[],"preserved_by_key":["value"],"container":false,"padding":true,"autoscroll":false,"name":"html","_selectable":false},"skip_api":false,"component_class_id":"42fa1133b1928d16a86f4bd1a5ac9b444ba8baf37b247373f67eca5a7420b7ee","key":null,"api_info":{"type":"string"},"api_info_as_input":{"type":"string"},"api_info_as_output":{"type":"string"},"example_inputs":"<p>Hello</p>"},{"id":2,"type":"markdown","props":{"value":"GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc). (See our [paper](https://arxiv.org/abs/2311.12983) for more details.)\n\n## Data\nGAIA is made of more than 450 non-trivial question with an unambiguous answer, requiring different levels of tooling and autonomy to solve. \nIt is therefore divided in 3 levels, where level 1 should be breakable by very good LLMs, and level 3 indicate a strong jump in model capabilities. Each level is divided into a fully public dev set for validation, and a test set with private answers and metadata. \n\nGAIA data can be found in [this dataset](https://huggingface.co/datasets/gaia-benchmark/GAIA). Questions are contained in `metadata.jsonl`. Some questions come with an additional file, that can be found in the same folder and whose id is given in the field `file_name`.\n\n**Please do not repost the public dev set, nor use it in training data for your models.**\n\n## Leaderboard\nSubmission made by our team are labelled \"GAIA authors\". While we report average scores over different runs when possible in our paper, we only report the best run in the leaderboard.\n\nSee below for submissions.","show_label":false,"rtl":false,"latex_delimiters":[{"left":"$$","right":"$$","display":true}],"visible":true,"elem_classes":["markdown-text"],"preserved_by_key":["value"],"sanitize_html":true,"line_breaks":false,"header_links":false,"show_copy_button":false,"container":false,"padding":false,"name":"markdown","_selectable":false},"skip_api":false,"component_class_id":"c2f35e73b9c0ffb3ea6b3682c71039be03350e8cd5cf185fdbbc33d52e7a2945","key":null,"api_info":{"type":"string"},"api_info_as_input":{"type":"string"},"api_info_as_output":{"type":"string"},"example_inputs":"# Hello!"},{"id":3,"type":"row","props":{"variant":"default","visible":true,"equal_height":false,"show_progress":false,"preserved_by_key":[],"name":"row"},"skip_api":true,"component_class_id":"bc47bf2e53002b34f9ec79dc4a3052e03866c51adfd499cc985f9fa94a4de216","key":null},{"id":4,"type":"accordion","props":{"label":"📙 Citation","open":false,"visible":true,"preserved_by_key":[],"name":"accordion"},"skip_api":true,"component_class_id":"68fbff65480149a50fe591fef1948bbaed634b810b18b905b01f834add9fefe5","key":null},{"id":5,"type":"textbox","props":{"value":"@misc{mialon2023gaia,\n      title={GAIA: a benchmark for General AI Assistants}, \n      author={Grégoire Mialon and Clémentine Fourrier and Craig Swift and Thomas Wolf and Yann LeCun and Thomas Scialom},\n      year={2023},\n      eprint={2311.12983},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}","type":"text","lines":1,"label":"Copy the following snippet to cite these results","show_label":true,"container":true,"min_width":160,"visible":true,"elem_id":"citation-button","autofocus":false,"autoscroll":true,"elem_classes":[],"preserved_by_key":["value"],"rtl":false,"show_copy_button":false,"submit_btn":false,"stop_btn":false,"name":"textbox","_selectable":false},"skip_api":false,"component_class_id":"ad9c3f714247746291346c2a8132a8d15ab68326a3bb22df88fcb9718ea34ecf","key":null,"api_info":{"type":"string"},"api_info_as_input":{"type":"string"},"api_info_as_output":{"type":"string"},"example_inputs":"Hello!!"},{"id":6,"type":"form","props":{"scale":0,"min_width":0,"preserved_by_key":[],"name":"form"},"skip_api":true,"component_class_id":"7c96e70477dd55732fb1a4bc6696ddcc8ff96faeabe823f8c3311f0d1f92d0eb","key":null},{"id":7,"type":"markdown","props":{"value":"Results: Test","show_label":false,"rtl":false,"latex_delimiters":[{"left":"$$","right":"$$","display":true}],"visible":true,"elem_classes":[],"preserved_by_key":["value"],"sanitize_html":true,"line_breaks":false,"header_links":false,"show_copy_button":false,"container":false,"padding":false,"name":"markdown","_selectable":false},"skip_api":false,"component_class_id":"c2f35e73b9c0ffb3ea6b3682c71039be03350e8cd5cf185fdbbc33d52e7a2945","key":null,"api_info":{"type":"string"},"api_info_as_input":{"type":"string"},"api_info_as_output":{"type":"string"},"example_inputs":"# Hello!"},{"id":8,"type":"dataframe","props":{"value":{"headers":["Agent name","Model family","organisation","Average score (%)","Level 1 score (%)","Level 2 score (%)","Level 3 score (%)","Submission date"],"data":[["<a target=\"_blank\" href=\"https://github.com/tosky001/OpenSearch-Agentic-Search\" style=\"color: var(--link-text-color); 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text-decoration: underline;text-decoration-style: dotted;\">SHAPARTNER V11</a>","GPT  Claude  Gemini  DeepSeek","SHAPARTNER",92.03,97.85,90.57,85.71,"2026-09-22"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Cobra Agent Orch</a>","GPT 5.5, Gemini 3 Pro, Claude Opus 4.8","Dalpha Inc",92.03,98.92,88.68,89.8,"2026-06-23"],["<a target=\"_blank\" href=\"https://github.com/adorosario/customgpt-agent\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab V42</a>","CustomGPT.ai Enterprise Agents","CustomGPT.ai",92.03,96.77,89.94,89.8,"2026-05-22"],["<a target=\"_blank\" href=\"https://github.com/ZTE-AICloud/Co-Sight\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Co-Sight Pro v1.0.0</a>","ZTE Nebula LLM, claude-opus-4.7, gemini-3.1-pro-preview","ZTE-AICloud",92.03,96.77,90.57,87.76,"2026-05-11"],["<a target=\"_blank\" href=\"https://github.com/adorosario/customgpt-manus-agent\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab v43</a>","Claude","CustomGPT.ai",92.03,96.77,90.57,87.76,"2026-05-23"],["<a target=\"_blank\" href=\"https://github.com/adorosario/customgpt-agent\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab v41</a>","Claude, GPT, Gemini","CustomGPT.ai",92.03,96.77,89.94,89.8,"2026-05-22"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Cobra Agent_v3.2</a>","GPT 5.5","Dalpha Inc",91.69,98.92,88.68,87.76,"2026-06-23"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Cobra Agent_v3.3</a>","GPT 5.5","Dalpha Inc",91.69,98.92,88.68,87.76,"2026-06-23"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Cobra Agent_v3.2.2</a>","GPT 5.5","Dalpha Inc",91.69,98.92,88.68,87.76,"2026-06-23"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); 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text-decoration: underline;text-decoration-style: dotted;\">openJiuwen-deepagent</a>","GPT5, Gemini-3-Pro","openJiuwen",91.69,98.92,88.68,87.76,"2026-02-09"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Cobra Agent_v3.2.1</a>","GPT 5.5","Dalpha Inc",91.69,98.92,88.68,87.76,"2026-06-23"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Cobra Agent_v3.2.3</a>","GPT 5.5","Dalpha Inc",91.69,98.92,88.68,87.76,"2026-06-23"],["<a target=\"_blank\" href=\"https://github.com/adorosario/customgpt-agent\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab v39</a>","Claude","CustomGPT.ai",91.69,96.77,89.94,87.76,"2026-05-21"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Cobra Agent_v3.1</a>","GPT 5.5","Dalpha Inc",91.69,98.92,88.68,87.76,"2026-06-23"],["<a target=\"_blank\" href=\"https://github.com/adorosario/customgpt-agent\" style=\"color: var(--link-text-color); 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text-decoration: underline;text-decoration-style: dotted;\">Lemon</a>","GPT5, Gemini-3-pro,  o3","LR AILab of Lenovo CTO Org",91.36,96.77,89.31,87.76,"2026-02-06"],["<a target=\"_blank\" href=\"https://github.com/adorosario/customgpt-agent\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab v34</a>","Claude","CustomGPT.ai",91.36,96.77,89.31,87.76,"2026-05-19"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Cobra Agent_2.7</a>","GPT 5.5","Dalpha Inc",91.36,98.92,88.05,87.76,"2026-06-19"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Shadow Harness V1.08</a>","GPT  Claude  Grok  Gemini  DeepSeek","Shadow ",91.03,93.55,91.19,85.71,"2026-10-09"],["<a target=\"_blank\" href=\"https://github.com/adorosario/customgpt-agent\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab v33</a>","Claude","CustomGPT.ai",91.03,96.77,88.68,87.76,"2026-05-18"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">SHADOW HARNESS V10</a>","Claude GPT  Grok","SHADOW",91.03,97.85,89.94,81.63,"2026-09-19"],["<a target=\"_blank\" href=\"https://rysx.com.cn\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Shadow Harness  V12</a>","GPT Claude Grok","Shadow Harness",91.03,97.85,89.31,83.67,"2026-09-21"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Shadow Harness V9</a>","Claude  GPT  Grok","Shadow",91.03,96.77,90.57,81.63,"2026-09-18"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Harness V8</a>","Grok","Harness",91.03,97.85,89.94,81.63,"2026-09-18"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Shapartner V6</a>","Grok Claude ","Shapartne",91.03,96.77,89.94,83.67,"2026-09-19"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab v36</a>","Claude, GPT, Gemini","CustomGPT.ai",90.7,96.77,88.05,87.76,"2026-05-21"],["<a target=\"_blank\" href=\"https://github.com/adorosario/customgpt-agent\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab v31</a>","claude, gemini, gpt","CustomGPT.ai",90.7,96.77,88.68,85.71,"2026-05-15"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab v29</a>","claude, gemini, gpt","CustomGPT.ai",90.7,96.77,88.68,85.71,"2026-05-14"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">SHAPARTNER V10</a>","GPT  Grok Claude","SHAPARTNER",90.7,95.7,89.94,83.67,"2026-09-22"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab v28</a>","Claude, GPT, Gemini","CustomGPT.ai",90.7,96.77,88.68,85.71,"2026-05-14"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab V37</a>","claude, gemini, gpt","CustomGPT.ai",90.7,96.77,88.05,87.76,"2026-05-21"],["<a target=\"_blank\" href=\"https://github.com/adorosario/customgpt-agent\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab v32</a>","Claude","CustomGPT.ai",90.7,96.77,88.05,87.76,"2026-05-18"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Shadow Harness V8</a>","Claude GPT  Grok","Shadow",90.7,95.7,90.57,81.63,"2026-09-16"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Deep_Ensemble_1.2</a>","GPT 5.5, Claude","0",90.7,96.77,88.05,87.76,"2026-06-23"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">JoinAI_V2.2</a>","GPT 5, Gemini 3 Pro, DeepSeek 3.1, Qwen 3","JoinAI-CMCC",90.7,98.92,86.79,87.76,"2026-01-14"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">as_v2</a>","","",90.37,96.77,89.31,81.63,"2026-08-13"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">CustomGPT.ai Research Lab v30</a>","Claude","CustomGPT.ai",90.37,96.77,88.05,85.71,"2026-05-15"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">SHAPARTNER V4</a>","GPT  CLAUDE  GEMINI    DEEPSEEK","SHAPARTNER ",90.37,95.7,90.57,79.59,"2026-09-14"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Harness V5</a>","Grok","Harness ",90.37,94.62,90.57,81.63,"2026-09-17"],["<a target=\"_blank\" href=\"\" style=\"color: var(--link-text-color); 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Scores are expressed as the percentage of correct answers for a given split. \n\nEach question calls for an answer that is either a string (one or a few words), a number, or a comma separated list of strings or floats, unless specified otherwise. There is only one correct answer. \nHence, evaluation is done via quasi exact match between a model’s answer and the ground truth (up to some normalization that is tied to the “type” of the ground truth).\n\nIn our evaluation, we use a system prompt to instruct the model about the required format:\n```\nYou are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. 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The first two fields are mandatory, `reasoning_trace` is optional:\n```\n{\"task_id\": \"task_id_1\", \"model_answer\": \"Answer 1 from your model\", \"reasoning_trace\": \"The different steps by which your model reached answer 1\"}\n{\"task_id\": \"task_id_2\", \"model_answer\": \"Answer 2 from your model\", \"reasoning_trace\": \"The different steps by which your model reached answer 2\"}\n```\n\nOur scoring function can be found 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