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reasoning benchmark

TAU3-Bench Leaderboard

TAU3-Bench evaluates general-purpose agent capabilities through multi-turn interactions with simulated user models, retrieval, and complex decision-making scenarios.

Updated Sep 5, 2026

Models8
Model coverage8
MetricScore
EvidenceB

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TAU3-Bench Ranking

Higher score ranks better on this benchmark.

8 rows
Columns

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Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelXIMiMo-V2.5-ProXiaomiScore72.90%Percentile100.00%Participants8EvidenceCEvaluatedSep 8, 2026
Rank02ModelACQwen3.6 PlusAlibaba Cloud / Qwen TeamScore70.70%Percentile85.71%Participants8EvidenceCEvaluatedSep 8, 2026
Rank03ModelZAGLM-5.1Zhipu AIScore70.60%Percentile71.43%Participants8EvidenceCEvaluatedSep 8, 2026
Rank04ModelACQwen3.6-35B-A3BAlibaba Cloud / Qwen TeamScore67.20%Percentile57.14%Participants8EvidenceCEvaluatedSep 8, 2026
Rank05ModelIBIBM Granite 4.2 30BIBMScore62.00%Percentile42.86%Participants8EvidenceCEvaluatedSep 8, 2026
Rank06ModelIBIBM Granite 4.2 8BIBMScore58.06%Percentile28.57%Participants8EvidenceCEvaluatedSep 8, 2026
Rank07ModelIBIBM Granite 4.2 3BIBMScore45.78%Percentile14.29%Participants8EvidenceCEvaluatedSep 8, 2026
Rank08ModelNVNemotron 3 Ultra (550B A55B)NVIDIAScore22.60%Percentile0.00%Participants8EvidenceCEvaluatedSep 8, 2026

TAU3-Bench Highlights

The leading models and scores on this benchmark.

TAU3-Bench Score Distribution

A closer view of the leading scores on this benchmark.

TAU3-Bench

The Top AI Models for TAU3-Bench

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

What is TAU3-Bench?

What TAU3-Bench measures and how its scores work.

TAU3-Bench is a benchmark for evaluating general-purpose agent capabilities.

It measures performance in multi-turn interactions with simulated user models, retrieval, and complex decision-making scenarios, using the Score metric expressed as a ratio.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
TAU3-Bench
Modality
text
Primary category
reasoning
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 TAU3-Bench.

Which model scores highest on TAU3-Bench?

MiMo-V2.5-Pro is currently ranked first with 72.90%.

What are the top three models on TAU3-Bench?

The current leaders are MiMo-V2.5-Pro (72.90%), Qwen3.6 Plus (70.70%), and GLM-5.1 (70.60%).

Which TAU3-Bench model has the lowest official input price?

Qwen3.6-35B-A3B has the lowest matched official input price at $0.25 input / $1.5 output per 1M tokens.

Which models are fastest among TAU3-Bench results?

The fastest matched records are MiMo-V2.5-Pro (5.27 tok/s via DeepInfra).

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 TAU3-Bench measure?

It measures performance in multi-turn interactions with simulated user models, retrieval, and complex decision-making scenarios, using the Score metric expressed as a ratio.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

8 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 #1MiMo-V2.5-Pro72.90%
Rank #2Qwen3.6 Plus70.70%
Rank #3GLM-5.170.60%
Rank #4Qwen3.6-35B-A3B67.20%

Ranking basisThis tau3-bench 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
    XI
    MiMo-V2.5-ProXiaomi
    Score
    72.90%
    Price
    $0.44 input / $0.87 output per 1M tokens
    Speed
    Up to 5.27 tok/s via DeepInfra

    Strengths

    • Ranks #1 of 8 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures TAU3-Bench, not total model capability
  2. 02
    AC
    Qwen3.6 PlusAlibaba Cloud / Qwen Team
    Score
    70.70%
    Price
    $0.50 input / $3.0 output per 1M tokens

    Strengths

    • Ranks #2 of 8 compared models
    • 86th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures TAU3-Bench, not total model capability
  3. 03
    ZA
    Zhipu AI
    Score
    70.60%
    Price
    $1.4 input / $4.4 output per 1M tokens

    Strengths

    • Ranks #3 of 8 compared models
    • 71th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures TAU3-Bench, not total model capability
  4. 04
    AC
    Alibaba Cloud / Qwen Team
    Score
    67.20%
    Price
    $0.25 input / $1.5 output per 1M tokens

    Strengths

    • Ranks #4 of 8 compared models
    • 57th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures TAU3-Bench, not total model capability
  5. 05
    IB
    IBM
    Score
    62.00%

    Strengths

    • Ranks #5 of 8 compared models
    • 43th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures TAU3-Bench, not total model capability

Selection summary

Best AI Models for TAU3-Bench

MiMo-V2.5-Pro currently leads TAU3-Bench with 72.90%. 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.

GLM-5.1
Qwen3.6-35B-A3B
IBM Granite 4.2 30B
Benchmark rank #1MiMo-V2.5-Pro72.90% · $0.44 input / $0.87 output per 1M tokens
Benchmark rank #2Qwen3.6 Plus70.70% · $0.50 input / $3.0 output per 1M tokens
Benchmark rank #3GLM-5.170.60% · $1.4 input / $4.4 output per 1M tokens