reasoning benchmark
Wild Bench
WildBench is an automated evaluation framework that benchmarks large language models using 1,024 challenging, real-world tasks selected from over one million human-chatbot conversation logs. It introduces two evaluation metrics (WB-Reward and WB-Score) that achieve high correlation with human preferences and uses task-specific checklists for systematic evaluation.
Updated Aug 11, 2026
Models8
Model coverage8
MetricScore
EvidenceB
Wild Bench Ranking
Higher score ranks better on this benchmark.
| 1 | MA | 68.5% | 100.0% | 8 | C | |
| 2 | MA | 68.5% | 85.7% | 8 | C | |
| 3 | MA | 66.8% | 71.4% | 8 | C | |
| 4 | MA | 65.3% | 57.1% | 8 | C | |
| 5 | MA | 56.8% | 42.9% | 8 | C | |
| 6 | MA | 52.2% | 28.6% | 8 | C | |
| 7 | AL | 48.5% | 14.3% | 8 | C | |
| 8 | AL | 42.4% | 0.0% | 8 | C |
Wild Bench Score Distribution
A closer view of the leading scores on this benchmark.
Wild Bench Highlights
The leading models and scores on this benchmark.
What is Wild Bench?
What Wild Bench measures and how its scores work.
WildBench is an automated evaluation framework that benchmarks large language models using 1,024 challenging, real-world tasks selected from over one million human-chatbot conversation logs. It introduces two evaluation metrics (WB-Reward and WB-Score) that achieve high correlation with human preferences and uses task-specific checklists for systematic evaluation.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Wild Bench
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- wild-bench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Wild Bench.
Which model scores highest on Wild Bench?
MiniStral 3 (14B Instruct 2512) is currently ranked first with 68.5%.
What does Wild Bench measure?
WildBench is an automated evaluation framework that benchmarks large language models using 1,024 challenging, real-world tasks selected from over one million human-chatbot conversation logs. It introduces two evaluation metrics (WB-Reward and WB-Score) that achieve high correlation with human preferences and uses task-specific checklists for systematic evaluation.
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?
Yes. This benchmark can contribute to the current LLMBoard capability score.