language benchmark
WMT24++
WMT24++ is a comprehensive multilingual machine translation benchmark that expands the WMT24 dataset to cover 55 languages and dialects. It includes human-written references and post-edits across four domains (literary, news, social, and speech) to evaluate machine translation systems and large language models across diverse linguistic contexts.
Updated Aug 11, 2026
Models23
Model coverage23
MetricScore
EvidenceB
WMT24++ Ranking
Higher score ranks better on this benchmark.
WMT24++ Score Distribution
A closer view of the leading scores on this benchmark.
WMT24++ Highlights
The leading models and scores on this benchmark.
What is WMT24++?
What WMT24++ measures and how its scores work.
WMT24++ is a comprehensive multilingual machine translation benchmark that expands the WMT24 dataset to cover 55 languages and dialects. It includes human-written references and post-edits across four domains (literary, news, social, and speech) to evaluate machine translation systems and large language models across diverse linguistic contexts.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- WMT24++
- Modality
- text
- Primary category
- language
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- wmt24++|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about WMT24++.
Which model scores highest on WMT24++?
Nemotron 3 Super (120B A12B) is currently ranked first with 86.7%.
What does WMT24++ measure?
WMT24++ is a comprehensive multilingual machine translation benchmark that expands the WMT24 dataset to cover 55 languages and dialects. It includes human-written references and post-edits across four domains (literary, news, social, and speech) to evaluate machine translation systems and large language models across diverse linguistic contexts.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
23 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.