TReB

Leaderboard (LLM-as-a-judge)

The results are generated through the native TReB evaluation process, taking the highest score among the three modes: TCoT, PoT, and ICoT. (We also encourage submitters to use custom workflows or inference modes.)

	Model	Table Understanding (TU)	Table Basic Operation (TBO)	Table Computational Operation (TCO)	Data Analysis (DA)	Advanced Data Analysis (ADA)	Overall Score
Oct 28, 2025	JT-DA JIUTIAN Research	80.704	88.88	83.335	83.335	53.05	78.64
Feb 3, 2026	doubao-1.5-pro ByteDance	79.996	87.12	79.9	90.5	54.818	78.47
Sep 1, 2025	QwQ-32B Alibaba	83.22	81.98	83.275	83.275	58.595	78.15
Sep 1, 2025	Qwen3-32B Alibaba	81.084	83.06	82.78	84.415	57.003	77.67
Feb 3, 2026	gpt-4o OpenAI	81.09	80.21	81.145	89.288	56.22	77.59
Sep 1, 2025	Qwen2.5-72B-Instruct Alibaba	80.462	80.17	80.225	85.093	53.555	75.9
Sep 1, 2025	Qwen3-14B Alibaba	81.506	84.6	82.17	76.405	52.225	75.38
Feb 3, 2026	gpt-4o-mini OpenAI	75.496	76.88	78.91	84.85	49.062	73.04
Sep 1, 2025	Deepseek-R1-Distill-Qwen-32B Deepseek	76.914	77.85	77.41	81.545	49.948	72.73
Sep 1, 2025	Llama-3.1-70B-Instruct Meta	68.012	78.9	77.595	81.06	56.223	72.36
Sep 1, 2025	Qwen3-8B Alibaba	79.052	80.03	78.465	71.32	49.098	71.59
Sep 1, 2025	TableGPT2-7B Zhejiang University	71.632	71.00	74.685	85.12	48.212	70.13
Sep 1, 2025	Deepseek-R1-Distill-Qwen-14B Deepseek	75.028	74.12	72.76	73.73	39.18	66.96
Sep 1, 2025	Table-R1-Zero-7B Yale	73.53	60.655	73.655	80.07	45.853	66.75
Dec 2, 2025	DeepSeek-R1-0528-Qwen3-8B DeepSeek	77.292	78.21	69.035	64.273	40.387	65.84
Sep 1, 2025	Qwen2.5-Coder-7B-Instruct Alibaba	67.612	73.99	68.02	80.025	38.832	65.7
Sep 1, 2025	Qwen2.5-7B-Instruct Alibaba	69.052	68.23	67.3	79.755	41.915	65.25
Sep 1, 2025	Seed-Coder-8B-Instruct ByteDance	64.698	71.16	66.17	82.085	41.582	65.14
Sep 1, 2025	Qwen2.5-Math-72B-Instruct Alibaba	74.416	66.24	69.24	75.573	38.897	64.87
Sep 1, 2025	Llama-3.1-8B-Instruct Meta	59.4	70.13	63.81	76.445	35.35	61.03
Sep 1, 2025	Deepseek-R1-Distill-Qwen-7B Deepseek	60.72	58.58	53.855	71.463	29.313	54.79
Sep 1, 2025	Deepseek-R1-Distill-Llama-8B Deepseek	62.689	56.52	47.8	50.773	19.97	47.55
Sep 1, 2025	Yi-Coder-9B-Chat 01-AI	43.97	57.05	47.01	56.095	33.172	47.46
Sep 1, 2025	Table-R1-SFT-7B Yale	60.404	23.46	24.1	43.01	37.603	37.72

About TReB

TReB is a comprehensive table reasoning evolution benchmark, which measures both shallow table understanding abilities and deep table reasoning abilities. Overall, we construct a high quality dataset to evaluate 5 core skills of LLMs: Table Understanding (TU), Table Basic Operation (TBO), Table Computational Operation (TCO), Data Analysis (DA), and Advanced Data Analysis (ADA). Accordingly, we propose a total of 20 sub-tasks. The evaluation framework supports 3 distinct inference modes, TCoT, PoT and ICoT, encouraging more robust reasoning.

News

Feb. 3, 2026:
We have updated the evaluation results of some models on the leaderboard and removed the display of the Rouge-L metric, retaining only the LLM-as-a-judge metric (which is relatively fairer).
Jun. 18, 2025:
🔥🔥 The benchmark paper, code and dataset are all released! Please check and submit your result to leaderboard!!

Thank you for all the feedback!!!

Challenges from TReB

Submission

🤗🤗 We warmly welcome submissions to our leaderboard, including both your own methods and contributions showcasing the latest model performance! TReB features two separate leaderboards. Please refer to the Submission Guidelines below for details, and submit your results as instructed to jttreb2025@gmail.com.

Citation

@misc{li2025trebcomprehensivebenchmarkevaluating,
title={TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models}, 
author={Ce Li and Xiaofan Liu and Zhiyan Song and Ce Chi and Chen Zhao and Jingjing Yang and Zhendong Wang and Kexin Yang and Boshen Shi and Xing Wang and Chao Deng and Junlan Feng},
year={2025},
eprint={2506.18421},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.18421}, 
}