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 7, 2026
Published models25
Registry coverage25
MetricScore
EvidenceB
TAU-bench Retail leaderboard
Sorted by the source-provided rank. Higher score is better according to the registry.
Score distribution
Top published rows on the benchmark's original scale.
TAU-bench Retail highlights
The top published results on this benchmark's own scale.
What is TAU-bench Retail?
Definition and scoring fields from the benchmark registry.
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. The current registry marks this benchmark as not independently verified with evidence level B.
- Family
- TAU-bench Retail
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- tau-bench-retail|llm-stats-current
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
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 unique published model results are currently shown.
Does this benchmark affect the overall score?
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.