reasoning benchmark
TAU-bench Retail
A benchmark for evaluating tool-agent-user interaction in retail environments. Tests language agents' ability to handle dynamic conversations with users while using domain-specific API tools and following policy guidelines. Evaluates agents on tasks like order cancellations, address changes, and order status checks through multi-turn conversations.
Updated Aug 11, 2026
Models25
Model coverage25
MetricScore
EvidenceB
TAU-bench Retail Ranking
Higher score ranks better on this benchmark.
TAU-bench Retail Score Distribution
A closer view of the leading scores on this benchmark.
TAU-bench Retail Highlights
The leading models and scores on this benchmark.
What is TAU-bench Retail?
What TAU-bench Retail measures and how its scores work.
A benchmark for evaluating tool-agent-user interaction in retail environments. Tests language agents' ability to handle dynamic conversations with users while using domain-specific API tools and following policy guidelines. Evaluates agents on tasks like order cancellations, address changes, and order status checks through multi-turn conversations.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- TAU-bench Retail
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- tau-bench-retail|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about TAU-bench Retail.
Which model scores highest on TAU-bench Retail?
Claude Sonnet 4.5 is currently ranked first with 86.2%.
What does TAU-bench Retail measure?
A benchmark for evaluating tool-agent-user interaction in retail environments. Tests language agents' ability to handle dynamic conversations with users while using domain-specific API tools and following policy guidelines. Evaluates agents on tasks like order cancellations, address changes, and order status checks through multi-turn conversations.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
25 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.