General Agents Benchmark

TauBench V3 Banking

TauBench V3 - Banking domain. Agentic tool use benchmark for banking customer service.

Category

General Agents

Score type

percent

Scale

100%

higher is better

Models tested

12

What is TauBench V3 Banking?

τ-bench Retail simulates a customer-service agent in a retail business. The model talks to a simulated user and must use the company's tools (orders, refunds, policy rules) to actually solve the customer's problem. Only when the database state ends up exactly right does the task count as solved.

What does it test?

TauBench V3 Banking measures how well a model acts as a general-purpose agent. In detail, it checks:

  • calling tools with correct arguments
  • following written policies while serving the simulated customer
  • multi-turn conversation behaviour (asking, confirming, acting)
  • correct final database results, not just polite replies

How to read the score

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Frequently asked questions

What is the TauBench V3 Banking benchmark?

τ-bench Retail simulates a customer-service agent in a retail business. The model talks to a simulated user and must use the company's tools (orders, refunds, policy rules) to actually solve the customer's problem. Only when the database state ends up exactly right does the task count as solved.

What does TauBench V3 Banking test?

TauBench V3 Banking mainly measures how well a model acts as a general-purpose agent. Concretely, it checks: calling tools with correct arguments; following written policies while serving the simulated customer; multi-turn conversation behaviour (asking, confirming, acting).

How is the TauBench V3 Banking score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get TauBench V3 Banking data and scores?

The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/taubench-v3-banking/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 12 models tested

Reset
# Model Score Date Source Verified
1 Kolibri-1
Aleph Alpha
38.1%
03.10.2026 Report Verified
2 Kimi K3
Moonshot AI
33.4%
18.08.2026 Report Verified
3 Ripple-65B Preview
Ricursive Intelligence
31.27%
— Report Verified
4 DeepSeek-V4-Flash
DeepSeek
26.7%
04.06.2026 Report Unverified
5 DeepSeek-V4-Pro
DeepSeek
25.9%
04.06.2026 Report Unverified
6 Muse-Glimmer-30B
Meta Superintelligence Lab
23.5%
01.08.2026 Meta Superintelligence Lab (official model card) Verified
7 Kimi K2.6
Moonshot AI
23.1%
04.06.2026 Report Unverified
8 NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
NVIDIA
22.6%
04.06.2026 Report Verified
9 Qwen3.6-27B
Qwen Team (Alibaba Cloud)
16.7%
01.08.2026 Meta Superintelligence Lab (official model card) Unverified
10 Gemma4-31B-it
Google
15.1%
01.08.2026 Meta Superintelligence Lab (official model card) Unverified
11 Muse-Spark-Open-816B-A42B
Meta Superintelligence Lab
14.4%
— Report Unverified
12 LFM2.5-24B-A2B
LiquidAI
8.04%
13.10.2026 Report Verified

What now?

You've seen how models score on TauBench V3 Banking — here's what to do next.