IndicBankBench - Confusing Intent: leaderboard

The confusing-intent axis of IndicBankBench (Paul et al., 2026): 53 retail-banking cases built around a confusing customer request. Percentage of cases passed in all three runs (strict pass^3), under deterministic safety and tool-use checks and a GLM-5.2 response judge; higher is better.

Metric: Strict pass^3 (%; 53 cases passed out of the 53 cases of the confusing-intent axis; each case run three times at temperature 0.7 with the stated reasoning modes; a case passes only when all three runs pass the deterministic safety and tool-use gates and the GLM-5.2 response judge; scripted Indian retail-banking conversations over deterministic mock tools). Source: arxiv.org. Saturation forecast: Not forecast. 11 models tracked.

Top models

#ModelScoreOverall rank
1GLM-5.3 Flash (High)71.7#119 (GLM-5.3 Flash)
2Grok 4.6 (High)64.15#44 (Grok 4.6)
3DeepSeek V4 Pro (0813) (Max)62.26#81 (DeepSeek V4 Pro (0813))
4Gemma 4 31B (IT) (Thinking)62.26#343 (Gemma 4 31B (IT))
5Gemma 4 E4B (IT) (Thinking)62.26
6DeepSeek V4 Flash (0731) (Max)60.38#136 (DeepSeek V4 Flash (0731))
7MiniMax-M356.6#265
8Gemini 3.8 Flash56.6#36
9gemma-4-26B-A4B-it (Thinking)56.6
10Claude Opus 5 (Max)52.83#20 (Claude Opus 5)

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

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