CITBench - Multi-Input: leaderboard

CITBench (2026) evaluates LLMs on tabular data processing tasks of matching, cleaning, augmentation and transformation. Scored in the offline single-turn setting as average performance: the mean Level-Accuracy over repeated runs, with partial credit, in percent; higher is better. This board covers the multi-input tier, where several tables must be combined.

Metric: Average performance (%; mean Level-Accuracy with partial credit, each of repeated runs scored 0 to 1, on the offline single-turn tabular data processing tasks of CITBench, a balanced 50% slice of its 1,296 instances in 18 matching, cleaning, augmentation and transformation task types; temperature 1.0, 8,192 output tokens; Multi-Input tier: several input tables). Source: arxiv.org. Saturation forecast: Around February 2027. 12 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 3 Pro62.63#135
2Claude Sonnet 4.562.5#244
3Gemini 3 Flash60.16#159
4GPT-5.257.7#172
5GLM-4.756.04#365
6GPT-4o49.62#741
7Claude Haiku 4.549.04#568
8Qwen 3 32B41.38#960
9Qwen 3 Coder Flash39.76#1624
10Qwen 3 235B A22B38.06#622
11Qwen 3 14B36.86#1235
12DeepSeek V3.230.04#403

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

Interactive version: theaggregate.ai/benchmark?slug=citbench-multi-input · How It Works · Data refreshed daily, snapshot 2026-11-12.