language benchmark
WMT23
The Eighth Conference on Machine Translation (WMT23) benchmark evaluating machine translation systems across 8 language pairs (14 translation directions) including general, biomedical, literary, and low-resource language translation tasks. Features specialized shared tasks for quality estimation, metrics evaluation, sign language translation, and discourse-level literary translation with professional human assessment.
Updated Aug 11, 2026
Models4
Model coverage4
MetricScore
EvidenceB
WMT23 Ranking
Higher score ranks better on this benchmark.
| 01 | GO | 75.1% | 100.0% | 4 | C | |
| 02 | GO | 74.1% | 66.7% | 4 | C | |
| 03 | GO | 72.6% | 33.3% | 4 | C | |
| 04 | GO | 71.7% | 0.0% | 4 | B |
WMT23 Score Distribution
A closer view of the leading scores on this benchmark.
WMT23 Highlights
The leading models and scores on this benchmark.
What is WMT23?
What WMT23 measures and how its scores work.
The Eighth Conference on Machine Translation (WMT23) benchmark evaluating machine translation systems across 8 language pairs (14 translation directions) including general, biomedical, literary, and low-resource language translation tasks. Features specialized shared tasks for quality estimation, metrics evaluation, sign language translation, and discourse-level literary translation with professional human assessment.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- WMT23
- Modality
- text
- Primary category
- language
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- wmt23|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about WMT23.
Which model scores highest on WMT23?
Gemini 1.5 Pro is currently ranked first with 75.1%.
What does WMT23 measure?
The Eighth Conference on Machine Translation (WMT23) benchmark evaluating machine translation systems across 8 language pairs (14 translation directions) including general, biomedical, literary, and low-resource language translation tasks. Features specialized shared tasks for quality estimation, metrics evaluation, sign language translation, and discourse-level literary translation with professional human assessment.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
4 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.