reasoning benchmark
MT-Bench
MT-Bench is a challenging multi-turn benchmark that measures the ability of large language models to engage in coherent, informative, and engaging conversations. It uses strong LLMs as judges for scalable and explainable evaluation of multi-turn dialogue capabilities.
Updated Aug 11, 2026
Models12
Model coverage12
MetricScore
EvidenceB
MT-Bench Ranking
Higher score ranks better on this benchmark.
| 1 | AC | 0.935 points | 100.0% | 12 | C | |
| 2 | NV | 0.917 points | 90.9% | 12 | C | |
| 3 | DE | 0.902 points | 81.8% | 12 | C | |
| 4 | NR | 8.99 points | 72.7% | 12 | C | |
| 5 | AC | 0.875 points | 63.6% | 12 | C | |
| 6 | MA | 0.863 points | 54.5% | 12 | C | |
| 7 | AC | 0.841 points | 45.5% | 12 | C | |
| 8 | MA | 0.835 points | 36.4% | 12 | C | |
| 9 | MA | 0.83 points | 27.3% | 12 | C | |
| 10 | NV | 0.81 points | 18.2% | 12 | C | |
| 11 | MA | 0.768 points | 9.1% | 12 | C | |
| 12 | NV | 0.09 points | 0.0% | 12 | C |
MT-Bench Score Distribution
A closer view of the leading scores on this benchmark.
MT-Bench Highlights
The leading models and scores on this benchmark.
What is MT-Bench?
What MT-Bench measures and how its scores work.
MT-Bench is a challenging multi-turn benchmark that measures the ability of large language models to engage in coherent, informative, and engaging conversations. It uses strong LLMs as judges for scalable and explainable evaluation of multi-turn dialogue capabilities.
Scores are shown in points. This benchmark is not independently verified and has an evidence level of B.
- Family
- MT-Bench
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- mt-bench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about MT-Bench.
Which model scores highest on MT-Bench?
Qwen2.5 72B Instruct is currently ranked first with 0.935 points.
What does MT-Bench measure?
MT-Bench is a challenging multi-turn benchmark that measures the ability of large language models to engage in coherent, informative, and engaging conversations. It uses strong LLMs as judges for scalable and explainable evaluation of multi-turn dialogue capabilities.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
12 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.