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chat benchmark

TAU-bench Retail Leaderboard

TAU-bench Retail evaluates language agents' tool-agent-user interaction in retail environments through multi-turn conversations using domain-specific API tools and policy guidelines.

Updated Sep 5, 2026

Models25
Model coverage25
MetricScore
EvidenceB

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TAU-bench Retail Ranking

Higher score ranks better on this benchmark.

25 rows
Columns

Show columns

Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelANClaude Sonnet 4.5AnthropicScore86.20%Percentile100.00%Participants25EvidenceCEvaluatedSep 8, 2026
Rank02ModelANClaude Opus 4.1AnthropicScore82.40%Percentile95.83%Participants25EvidenceCEvaluatedSep 8, 2026
Rank03ModelANClaude Opus 4AnthropicScore81.40%Percentile91.67%Participants25EvidenceCEvaluatedSep 8, 2026
Rank04ModelANClaude 3.7 SonnetAnthropicScore81.20%Percentile87.50%Participants25EvidenceCEvaluatedSep 8, 2026
Rank05ModelANClaude Sonnet 4AnthropicScore80.50%Percentile83.33%Participants25EvidenceCEvaluatedSep 8, 2026
Rank06ModelZAGLM-4.5Zhipu AIScore79.70%Percentile79.17%Participants25EvidenceCEvaluatedSep 8, 2026
Rank07ModelZAGLM-4.5-AirZhipu AIScore77.90%Percentile75.00%Participants25EvidenceCEvaluatedSep 8, 2026
Rank08ModelACQwen3-Coder 480B A35B InstructAlibaba Cloud / Qwen TeamScore77.50%Percentile70.83%Participants25EvidenceCEvaluatedSep 8, 2026
Rank09ModelOPo4-miniOpenAIScore71.80%Percentile66.67%Participants25EvidenceCEvaluatedSep 8, 2026
Rank10ModelOPo1OpenAIScore70.80%Percentile62.50%Participants25EvidenceCEvaluatedSep 8, 2026
Rank11ModelACQwen3-Next-80B-A3B-ThinkingAlibaba Cloud / Qwen TeamScore69.60%Percentile58.33%Participants25EvidenceCEvaluatedSep 8, 2026
Rank12ModelANClaude 3.5 SonnetAnthropicScore69.20%Percentile54.17%Participants25EvidenceCEvaluatedSep 8, 2026
Rank13ModelOPGPT-4.5OpenAIScore68.40%Percentile50.00%Participants25EvidenceCEvaluatedSep 8, 2026
Rank14ModelOPGPT-4.1OpenAIScore68.00%Percentile45.83%Participants25EvidenceCEvaluatedSep 8, 2026
Rank15ModelOPGPT OSS 120BOpenAIScore67.80%Percentile41.67%Participants25EvidenceCEvaluatedSep 8, 2026
Rank16ModelMIMiniMax M1 40KMiniMaxScore67.80%Percentile37.50%Participants25EvidenceCEvaluatedSep 8, 2026
Rank17ModelACQwen3-235B-A22B-Thinking-2507Alibaba Cloud / Qwen TeamScore67.80%Percentile33.33%Participants25EvidenceCEvaluatedSep 8, 2026
Rank18ModelMIMiniMax M1 80KMiniMaxScore63.50%Percentile29.17%Participants25EvidenceCEvaluatedSep 8, 2026
Rank19ModelACQwen3-Next-80B-A3B-InstructAlibaba Cloud / Qwen TeamScore60.90%Percentile25.00%Participants25EvidenceCEvaluatedSep 8, 2026
Rank20ModelOPGPT-4oOpenAIScore60.30%Percentile20.83%Participants25EvidenceCEvaluatedSep 8, 2026
Rank21ModelOPo3-miniOpenAIScore57.60%Percentile16.67%Participants25EvidenceCEvaluatedSep 8, 2026
Rank22ModelOPGPT-4.1 miniOpenAIScore55.80%Percentile12.50%Participants25EvidenceCEvaluatedSep 8, 2026
Rank23ModelOPGPT OSS 20BOpenAIScore54.80%Percentile8.33%Participants25EvidenceCEvaluatedSep 8, 2026
Rank24ModelANClaude 3.5 HaikuAnthropicScore51.00%Percentile4.17%Participants25EvidenceCEvaluatedSep 8, 2026
Rank25ModelOPGPT-4.1 nanoOpenAIScore22.60%Percentile0.00%Participants25EvidenceCEvaluatedSep 8, 2026

TAU-bench Retail Highlights

The leading models and scores on this benchmark.

TAU-bench Retail Score Distribution

A closer view of the leading scores on this benchmark.

TAU-bench Retail

The Top AI Models for TAU-bench Retail

The first five results on this benchmark, with official price and output speed added where the model identity can be matched.

What is TAU-bench Retail?

What TAU-bench Retail measures and how its scores work.

TAU-bench Retail is a benchmark for evaluating tool-agent-user interaction in retail environments.

It measures language agents' ability to handle dynamic multi-turn conversations with users, use domain-specific API tools, follow policy guidelines, and complete tasks such as order cancellations, address changes, and order status checks, reported as a Score ratio.

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
chat
Score direction
higher
LLMBoard eligible
No
Evaluation key
overall

LLM Stats. 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 86.20%.

What are the top three models on TAU-bench Retail?

The current leaders are Claude Sonnet 4.5 (86.20%), Claude Opus 4.1 (82.40%), and Claude Opus 4 (81.40%).

Which TAU-bench Retail model has the lowest official input price?

GPT-4.1 nano has the lowest matched official input price at $0.10 input / $0.40 output per 1M tokens.

Which models are fastest among TAU-bench Retail results?

The fastest matched records are GPT OSS 20B (1,000.00 tok/s via Groq), GPT OSS 120B (500.00 tok/s via Groq), and GPT-4.1 mini (467.39 tok/s via OpenAI).

Does the highest score result prove overall model quality?

No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.

What does TAU-bench Retail measure?

It measures language agents' ability to handle dynamic multi-turn conversations with users, use domain-specific API tools, follow policy guidelines, and complete tasks such as order cancellations, address changes, and order status checks, reported as a Score ratio.

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?

No. This benchmark is shown for reference but does not contribute to the overall score.

Rank #1Claude Sonnet 4.586.20%
Rank #2Claude Opus 4.182.40%
Rank #3Claude Opus 481.40%
Rank #4Claude 3.7 Sonnet81.20%

Ranking basisThis tau-bench retail AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    AN
    Claude Sonnet 4.5Anthropic
    Score
    86.20%
    Price
    $3.0 input / $15 output per 1M tokens
    Speed
    Up to 42.00 tok/s via Anthropic

    Strengths

    • Ranks #1 of 25 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures TAU-bench Retail, not total model capability
  2. 02
    AN
    Claude Opus 4.1Anthropic
    Score
    82.40%
    Speed
    Up to 100.00 tok/s via Anthropic

    Strengths

    • Ranks #2 of 25 compared models
    • 96th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures TAU-bench Retail, not total model capability
  3. 03
    AN
    Anthropic
    Score
    81.40%
    Speed
    Up to 100.00 tok/s via Anthropic

    Strengths

    • Ranks #3 of 25 compared models
    • 92th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures TAU-bench Retail, not total model capability
  4. 04
    AN
    Anthropic
    Score
    81.20%
    Speed
    Up to 42.00 tok/s via Anthropic

    Strengths

    • Ranks #4 of 25 compared models
    • 88th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures TAU-bench Retail, not total model capability
  5. 05
    AN
    Anthropic
    Score
    80.50%
    Speed
    Up to 42.00 tok/s via Anthropic

    Strengths

    • Ranks #5 of 25 compared models
    • 83th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures TAU-bench Retail, not total model capability

Selection summary

Best AI Models for TAU-bench Retail

Claude Sonnet 4.5 currently leads TAU-bench Retail with 86.20%. 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.

Claude Opus 4
Claude 3.7 Sonnet
Claude Sonnet 4
Benchmark rank #1Claude Sonnet 4.586.20% · $3.0 input / $15 output per 1M tokens
Benchmark rank #2Claude Opus 4.182.40% · Up to 100.00 tok/s via Anthropic
Benchmark rank #3Claude Opus 481.40% · Up to 100.00 tok/s via Anthropic