reasoning benchmark
Tau3 Airline evaluates agentic models on multi-turn, tool-using customer-support scenarios in a simulated airline booking and reservations environment.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score72.00% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis tau3 airline AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Mistral Medium 3.5 currently leads Tau3 Airline with 72.00%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What Tau3 Airline measures and how its scores work.
Tau3 Airline is a benchmark for evaluating agentic models in a simulated airline booking and reservations environment.
It measures performance using the Score metric, reported as a ratio, on multi-turn, tool-using customer-support scenarios.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about Tau3 Airline.
Mistral Medium 3.5 is currently ranked first with 72.00%.
The current leaders are Mistral Medium 3.5 (72.00%).
Mistral Medium 3.5 has the lowest matched official input price at $1.5 input / $7.5 output per 1M tokens.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures performance using the Score metric, reported as a ratio, on multi-turn, tool-using customer-support scenarios.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.