IndicBankBench - Multi-Tool Agentic: leaderboard

The general multi-tool agentic axis of IndicBankBench (Paul et al., 2026): 112 retail-banking cases that need several tool calls. 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 (%; 112 cases passed out of the 112 cases of the general multi-tool agentic 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 2028. 11 models tracked.

Top models

#ModelScoreOverall rank
1Claude Opus 5 (Max)42.86#20 (Claude Opus 5)
2GLM-5.3 Flash (High)39.29#119 (GLM-5.3 Flash)
3Gemini 3.8 Flash38.39#36
4DeepSeek V4 Pro (0813) (Max)37.5#81 (DeepSeek V4 Pro (0813))
5Grok 4.6 (High)37.5#44 (Grok 4.6)
6DeepSeek V4 Flash (0731) (Max)33.04#136 (DeepSeek V4 Flash (0731))
7Gemma 4 31B (IT) (Thinking)31.25#343 (Gemma 4 31B (IT))
8MiniMax-M324.11#265
9gemma-4-26B-A4B-it (Thinking)21.43
10Gemma 4 E4B (IT) (Thinking)19.64

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

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