reasoning benchmark
Tau-bench
τ-bench: A benchmark for tool-agent-user interaction in real-world domains. Tests language agents' ability to interact with users and follow domain-specific rules through dynamic conversations using API tools and policy guidelines across retail and airline domains. Evaluates consistency and reliability of agent behavior over multiple trials.
Updated Aug 11, 2026
Models6
Model coverage6
MetricScore
EvidenceB
Tau-bench Ranking
Higher score ranks better on this benchmark.
| 1 | ST | 88.2% | 100.0% | 6 | C | |
| 2 | ZA | 87.4% | 80.0% | 6 | C | |
| 3 | XI | 80.3% | 60.0% | 6 | C | |
| 4 | ZA | 79.5% | 40.0% | 6 | C | |
| 5 | MI | 77.2% | 20.0% | 6 | C | |
| 6 | OP | 63.0% | 0.0% | 6 | C |
Tau-bench Score Distribution
A closer view of the leading scores on this benchmark.
Tau-bench Highlights
The leading models and scores on this benchmark.
What is Tau-bench?
What Tau-bench measures and how its scores work.
τ-bench: A benchmark for tool-agent-user interaction in real-world domains. Tests language agents' ability to interact with users and follow domain-specific rules through dynamic conversations using API tools and policy guidelines across retail and airline domains. Evaluates consistency and reliability of agent behavior over multiple trials.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Tau-bench
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- tau-bench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Tau-bench.
Which model scores highest on Tau-bench?
Step-3.5-Flash is currently ranked first with 88.2%.
What does Tau-bench measure?
τ-bench: A benchmark for tool-agent-user interaction in real-world domains. Tests language agents' ability to interact with users and follow domain-specific rules through dynamic conversations using API tools and policy guidelines across retail and airline domains. Evaluates consistency and reliability of agent behavior over multiple trials.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
6 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.