General Agents Benchmark

TAU3-Bench

TAU3-Bench - agentic tool use benchmark. Uses official user model (gpt-5.2, low reasoning effort) + default BM25 retrieval.

Category

General Agents

Score type

percent

Scale

100%

higher is better

Models tested

20

What is TAU3-Bench?

TAU3-Bench is a benchmark for how well a model acts as a general-purpose agent. The official description says: "TAU3-Bench - agentic tool use benchmark". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

TAU3-Bench 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.

Official methodology note: TAU3-Bench - agentic tool use benchmark. Uses official user model (gpt-5.2, low reasoning effort) + default BM25 retrieval.

Frequently asked questions

What is the TAU3-Bench benchmark?

TAU3-Bench is a benchmark for how well a model acts as a general-purpose agent. The official description says: 'TAU3-Bench - agentic tool use benchmark'. Below we describe in simple words what benchmarks of this type generally examine.

What does TAU3-Bench test?

TAU3-Bench 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 TAU3-Bench 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 TAU3-Bench data and scores?

The official source is https://artificialanalysis.ai/evaluations/tau3-banking. 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/tau3-bench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 20 models tested

Reset
# Model Score Date Source Verified
1 Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
68.9%
16.04.2026 Qwen Team (official model card - comparison) Unverified
2 Qwen3.5-27B
Qwen Team (Alibaba Cloud)
68.4%
16.04.2026 Qwen Team (official model card - comparison) Unverified
3 LFM3-1.7B
LiquidAI
68.01%
— Report Verified
4 Gemma4-31B-it
Google
67.5%
16.04.2026 Qwen Team (official model card - comparison) Unverified
5 Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
67.2%
16.04.2026 Qwen Team (official model card) Verified
6 Xing4.0-29B-A4B
China Telecom AI
64.63%
16.09.2026 Report Verified
7 Gemma4-26B-A4B
Google
59%
16.04.2026 Qwen Team (official model card - comparison) Unverified
8 Nanbeige4.3-33B
Nanbeige
48.8%
24.10.2026 Report Verified
9 Spark-X2.5-4B
SparkLLM (XHToken)
30.4%
— Report Verified
10 MiniCPM5-2B
OpenBMB
20.8%
— Report Verified
11 Spark-X2.5-1.7B
SparkLLM (XHToken)
20.1%
— Report Verified
12 Gemma4-12B
Google
13.3%
— Report Verified
13 Gemma4-E4B
Google
10.1%
— Report Verified
14 Gemma4-E2B
Google
8.8%
— Report Verified
15 LFM2.5-2.6B
LiquidAI
7.2%
— Report Verified
16 Qwen3.5-4B
Qwen
6.7%
— Report Verified
17 granite-4.2-3B
IBM
5.6%
— Report Verified
18 Qwen3.5-2B
Qwen Team (Alibaba Cloud)
4.1%
— Report Verified
19 LFM2.5-8B-A1B
LiquidAI
3.4%
— Report Verified
20 Nemotron-3-Nano-4B
NVIDIA
1.2%
— Report Verified

What now?

You've seen how models score on TAU3-Bench — here's what to do next.