General Agents Benchmark

BankerToolBench

Category

General Agents

Score type

percent

Scale

100%

higher is better

Models tested

8

What is BankerToolBench?

BankerToolBench is a benchmark for how well a model acts as a general-purpose agent. It was built to compare language models on a clearly defined set of tasks under the same grading rules, so scores are directly comparable between models.

What does it test?

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

  • solving long, multi-step tasks with many intermediate decisions
  • using tools correctly (browsers, terminals, APIs, calendars, other apps)
  • adapting when something unexpected happens mid-task
  • staying on goal over long task horizons instead of wandering off

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 BankerToolBench benchmark?

BankerToolBench is a benchmark for how well a model acts as a general-purpose agent. It was built to compare language models on a clearly defined set of tasks under the same grading rules, so scores are directly comparable between models.

What does BankerToolBench test?

BankerToolBench mainly measures how well a model acts as a general-purpose agent. Concretely, it checks: solving long, multi-step tasks with many intermediate decisions; using tools correctly (browsers, terminals, APIs, calendars, other apps); adapting when something unexpected happens mid-task.

How is the BankerToolBench 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 BankerToolBench 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/bankertoolbench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 8 models tested

Reset
# Model Score Date Source Verified
1 Hy4 preview
Tencent
78.6%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) Verified
2 GLM-5.3
Zhipu AI
77.8%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) (Tencent rerun) Verified
3 MiniMax-M3.1-Flash
MiniMaxAI
76.2%
19.10.2026 Report Verified
4 MiniMax-M3
MiniMaxAI
76.1%
11.06.2026 MiniMax-M3 model card benchmark table Unverified
5 Qwen3.8-2.4T-A95B
Qwen
74.7%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) (Tencent rerun) Verified
6 DeepSeek-V4.1-Flash
DeepSeek
73.7%
19.10.2026 Report Unverified
7 Kimi K3
Moonshot AI
73.5%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) (Tencent rerun) Verified
8 Hy3
Tencent
68.8%
28.08.2026 Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) Verified

What now?

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