General Agents Benchmark

TAU2-Bench

TAU2-Bench - agentic tool use benchmark for customer service scenarios. Airline domain evaluated with fixes from Claude Opus 4.5 system card.

Category

General Agents

Score type

percent

Scale

relative

higher is better

Models tested

19

What is TAU2-Bench?

TAU2-Bench is a benchmark for how well a model acts as a general-purpose agent. The official description says: "TAU2-Bench - agentic tool use benchmark for customer service scenarios". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

TAU2-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: TAU2-Bench - agentic tool use benchmark for customer service scenarios. Airline domain evaluated with fixes from Claude Opus 4.5 system card.

Frequently asked questions

What is the TAU2-Bench benchmark?

TAU2-Bench is a benchmark for how well a model acts as a general-purpose agent. The official description says: 'TAU2-Bench - agentic tool use benchmark for customer service scenarios'. Below we describe in simple words what benchmarks of this type generally examine.

What does TAU2-Bench test?

TAU2-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 TAU2-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.

Where do I get TAU2-Bench 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/tau2-bench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 19 models tested

Reset
# Model Score Date Source Verified
1 MiniCPM5-2B
OpenBMB
97.1%
— Report Verified
2 LFM2.5-2.6B
LiquidAI
90.4%
— Report Verified
3 Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
81.2%
01.02.2026 Report Unverified
4 Qwen3.5-4B
Qwen
79.9%
01.02.2026 Report Verified
5 Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
79.5%
01.02.2026 Report Verified
6 Qwen3.5-27B
Qwen Team (Alibaba Cloud)
79%
01.02.2026 Report Unverified
7 Gemma4-31B-it
Google
76.9%
02.07.2026 Report Verified
8 Spark-X2.5-4B
SparkLLM (XHToken)
75.1%
— Report Verified
9 Gemma4-12B
Google
69%
02.07.2026 Report Verified
10 Gemma4-26B-A4B
Google
68.2%
02.07.2026 Report Verified
11 Spark-X2.5-1.7B
SparkLLM (XHToken)
65.3%
— Report Verified
12 Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
58.5%
01.02.2026 Report Unverified
13 Qwen3.5-2B
Qwen Team (Alibaba Cloud)
48.8%
— Report Verified
14 Gemma4-E4B
Google
42.2%
02.07.2026 Report Verified
15 granite-4.2-3B
IBM
40.9%
— Report Verified
16 Nemotron-3-Nano-4B
NVIDIA
28.1%
— Report Verified
17 Gemma4-E2B
Google
24.5%
02.07.2026 Report Verified
18 Gemma3-27B
Google
16.2%
02.07.2026 Report Unverified
19 LFM2.5-8B-A1B
LiquidAI
16.1%
— Report Verified

What now?

You've seen how models score on TAU2-Bench — here's what to do next.