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reasoning benchmark

Arena Hard

Arena-Hard-Auto is an automatic evaluation benchmark for instruction-tuned LLMs consisting of 500 challenging real-world prompts curated by BenchBuilder. It includes open-ended software engineering problems, mathematical questions, and creative writing tasks. The benchmark uses LLM-as-a-Judge methodology with GPT-4.1 and Gemini-2.5 as automatic judges to approximate human preference. Arena-Hard achieves 98.6% correlation with human preference rankings and provides 3x higher separation of model performances compared to MT-Bench, making it highly effective for distinguishing between models of similar quality.

Updated Aug 11, 2026

Models26
Model coverage26
MetricScore
EvidenceB

On this page

  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

Arena Hard Ranking

Higher score ranks better on this benchmark.

26 rows
Columns

Show columns

1ACQwen3 235B A22BAlibaba Cloud / Qwen Team95.6%100.0%26CAug 11, 2026
2ACQwen3 32BAlibaba Cloud / Qwen Team93.8%96.0%26CAug 11, 2026
3ACQwen3 30B A3BAlibaba Cloud / Qwen Team91.0%92.0%26CAug 11, 2026
4NVLlama-3.3 Nemotron Super 49B v1NVIDIA88.3%88.0%26CAug 11, 2026
5MAMistral Small 3 24B InstructMistral AI87.6%84.0%26CAug 11, 2026
6ACQwen2.5 72B InstructAlibaba Cloud / Qwen Team81.2%80.0%26CAug 11, 2026
7MIPhi 4 Reasoning PlusMicrosoft79.0%76.0%26CAug 11, 2026
8DEDeepSeek-V2.5DeepSeek76.2%72.0%26CAug 11, 2026
9MIPhi 4Microsoft75.4%68.0%26CAug 11, 2026
10MIPhi 4 ReasoningMicrosoft73.3%64.0%26CAug 11, 2026
11MAMinistral 8B InstructMistral AI70.9%60.0%26CAug 11, 2026
12ALJamba 1.5 LargeAI21 Labs65.4%56.0%26CAug 11, 2026
13MAMistral Small 4Mistral AI58.3%52.0%26CAug 11, 2026
14IBGranite 3.3 8B BaseIBM57.6%48.0%26CAug 11, 2026
15IBGranite 3.3 8B InstructIBM57.6%44.0%26CAug 11, 2026
16MAMiniStral 3 (14B Instruct 2512)Mistral AI55.1%40.0%26CAug 11, 2026
17MAMistral Large 3Mistral AI55.1%36.0%26CAug 11, 2026
18ACQwen2.5 7B InstructAlibaba Cloud / Qwen Team52.0%32.0%26CAug 11, 2026
19MAMinistral 3 (8B Instruct 2512)Mistral AI50.9%28.0%26CAug 11, 2026
20ALJamba 1.5 MiniAI21 Labs46.1%24.0%26CAug 11, 2026
21MAMistral Small 3.2 24B InstructMistral AI43.1%20.0%26CAug 11, 2026
22MIPhi-3.5-MoE-instructMicrosoft37.9%16.0%26CAug 11, 2026
23MIPhi-3.5-mini-instructMicrosoft37.0%12.0%26CAug 11, 2026
24MIPhi 4 MiniMicrosoft32.8%8.0%26CAug 11, 2026
25MAMinistral 3 (3B Instruct 2512)Mistral AI30.5%4.0%26CAug 11, 2026
26IBIBM Granite 4.0 Tiny PreviewIBM26.7%0.0%26CAug 11, 2026

Arena Hard Score Distribution

A closer view of the leading scores on this benchmark.

Arena Hard

Arena Hard Highlights

The leading models and scores on this benchmark.

Rank #1Qwen3 235B A22B95.6%Rank #2Qwen3 32B93.8%Rank #3Qwen3 30B A3B91.0%Rank #4Llama-3.3 Nemotron Super 49B v188.3%

What is Arena Hard?

What Arena Hard measures and how its scores work.

Arena-Hard-Auto is an automatic evaluation benchmark for instruction-tuned LLMs consisting of 500 challenging real-world prompts curated by BenchBuilder. It includes open-ended software engineering problems, mathematical questions, and creative writing tasks. The benchmark uses LLM-as-a-Judge methodology with GPT-4.1 and Gemini-2.5 as automatic judges to approximate human preference. Arena-Hard achieves 98.6% correlation with human preference rankings and provides 3x higher separation of model performances compared to MT-Bench, making it highly effective for distinguishing between models of similar quality.

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

Family
Arena Hard
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
arena-hard|llm-stats-current

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

FAQ

Common questions about Arena Hard.

Which model scores highest on Arena Hard?

Qwen3 235B A22B is currently ranked first with 95.6%.

What does Arena Hard measure?

Arena-Hard-Auto is an automatic evaluation benchmark for instruction-tuned LLMs consisting of 500 challenging real-world prompts curated by BenchBuilder. It includes open-ended software engineering problems, mathematical questions, and creative writing tasks. The benchmark uses LLM-as-a-Judge methodology with GPT-4.1 and Gemini-2.5 as automatic judges to approximate human preference. Arena-Hard achieves 98.6% correlation with human preference rankings and provides 3x higher separation of model performances compared to MT-Bench, making it highly effective for distinguishing between models of similar quality.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

26 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.

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