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reasoning benchmark

ACEBench Leaderboard

ACEBench is a comprehensive benchmark for evaluating Large Language Models' tool usage capabilities across three primary evaluation types: Normal (basic tool usage scenarios), Special (tool usage with ambiguous or incomplete instructions), and Agent (multi-agent interactions simulating real-world dialogues). The benchmark covers 4,538 APIs across 8 major domains and 68 sub-domains including technology, finance, entertainment, society, health, culture, and environment, supporting both English and Chinese languages.

Updated Aug 17, 2026

Models2
Model coverage2
MetricScore
EvidenceB

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ACEBench Ranking

Higher score ranks better on this benchmark.

2 rows
Columns

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Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelMAKimi K2 InstructMoonshot AIScore76.5%Percentile100.0%Participants2EvidenceCEvaluatedAug 17, 2026
Rank02ModelMAKimi K2-Instruct-0905Moonshot AIScore76.5%Percentile0.0%Participants2EvidenceCEvaluatedAug 17, 2026

ACEBench Highlights

The leading models and scores on this benchmark.

Rank #1Kimi K2 Instruct76.5%Rank #2Kimi K2-Instruct-090576.5%

ACEBench Score Distribution

A closer view of the leading scores on this benchmark.

ACEBench

The Top AI Models for ACEBench

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

Ranking basisThis acebench AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    MA
    Kimi K2 InstructMoonshot AI
    Score
    76.5%
    Price
    $0.60 input / $2.5 output per 1M tokens
    Speed
    Up to 45 tok/s via Novita

    Strengths

    • Ranks #1 of 2 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures ACEBench, not total model capability
  2. 02
    MA
    Kimi K2-Instruct-0905Moonshot AI
    Score
    76.5%
    Price
    $0.60 input / $2.5 output per 1M tokens
    Speed
    Up to 45 tok/s via Novita

    Strengths

    • Ranks #2 of 2 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures ACEBench, not total model capability

Selection summary

Best AI Models for ACEBench

Kimi K2 Instruct currently leads ACEBench with 76.5%. 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.

Benchmark rank #1Kimi K2 Instruct76.5% · $0.60 input / $2.5 output per 1M tokensBenchmark rank #2Kimi K2-Instruct-090576.5% · $0.60 input / $2.5 output per 1M tokens

What is ACEBench?

What ACEBench measures and how its scores work.

ACEBench is a comprehensive benchmark for evaluating Large Language Models' tool usage capabilities across three primary evaluation types: Normal (basic tool usage scenarios), Special (tool usage with ambiguous or incomplete instructions), and Agent (multi-agent interactions simulating real-world dialogues). The benchmark covers 4,538 APIs across 8 major domains and 68 sub-domains including technology, finance, entertainment, society, health, culture, and environment, supporting both English and Chinese languages.

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

Family
ACEBench
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
No
Evaluation key
acebench|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about ACEBench.

Which model scores highest on ACEBench?

Kimi K2 Instruct is currently ranked first with 76.5%.

What does ACEBench measure?

ACEBench is a comprehensive benchmark for evaluating Large Language Models' tool usage capabilities across three primary evaluation types: Normal (basic tool usage scenarios), Special (tool usage with ambiguous or incomplete instructions), and Agent (multi-agent interactions simulating real-world dialogues). The benchmark covers 4,538 APIs across 8 major domains and 68 sub-domains including technology, finance, entertainment, society, health, culture, and environment, supporting both English and Chinese languages.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

2 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.