BI-Bench (Python, BI-Agent Tools): leaderboard

BI-Bench (Hu et al., 2026) asks models to answer 100 business questions taken from real Power BI dashboards directly from the raw project tables, which requires finding, reshaping and joining the relevant tables before computing the answer. An answer is correct when its result table matches the ground truth in shape and values; accuracy in percent averaged over 10 runs, higher is better. Python setting with the BI-Agent search, transform and join tools of the authors added to the code-execution tool.

Metric: Accuracy (%; 100 end-to-end business-intelligence queries curated from real Power BI dashboards (82 projects, raw source tables needing search, reshaping, joins and analysis); an answer is correct when the output table matches the ground-truth table in shape and cell values up to row and column order, with numeric tolerance; mean over 10 runs; Python setting with the BI-Agent search, transform and join tools added to the code-execution tool). Source: arxiv.org. Saturation forecast: Around February 2027. 10 models tracked.

Top models

#ModelScoreOverall rank
1O4 Mini66.3#354
2GPT-5.565.4#48
3Kimi K2.662.7#173
4GPT-5.260.2#172
5DeepSeek V4 Pro57.6#181
6Mistral Large 351.1#819
7GPT-4o44.7#741
8Llama 4 Maverick38.8#1105
9GPT-OSS-120B27.3#699
10Qwen 3 8B13.1#1838

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=bi-bench-python-bi-agent-tools · How It Works · Data refreshed daily, snapshot 2026-11-12.