IndicBankBench - Irrelevant RAG: leaderboard

The irrelevant-retrieval axis of IndicBankBench (Paul et al., 2026): 24 retail-banking cases that supply irrelevant retrieved context. 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 (%; 24 cases passed out of the 24 cases of the irrelevant-retrieval 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: Around 2029. 11 models tracked.

Top models

#ModelScoreOverall rank
1Grok 4.6 (High)75#44 (Grok 4.6)
2Gemini 3.8 Flash70.83#36
3DeepSeek V4 Pro (0813) (Max)70.83#81 (DeepSeek V4 Pro (0813))
4DeepSeek V4 Flash (0731) (Max)70.83#136 (DeepSeek V4 Flash (0731))
5Gemma 4 31B (IT) (Thinking)70.83#343 (Gemma 4 31B (IT))
6gemma-4-26B-A4B-it (Thinking)70.83
7Gemma 4 E4B (IT) (Thinking)66.67
8MiniMax-M362.5#265
9GLM-5.3 Flash (High)62.5#119 (GLM-5.3 Flash)
10Claude Opus 5 (Max)58.33#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-irrelevant-rag · How It Works · Data refreshed daily, snapshot 2026-11-12.