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.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 12 models tested
| # | 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 |