language benchmark
Translation Set1→en COMET22
COMET-22 is a neural machine translation evaluation metric that uses an ensemble of two models: a COMET estimator trained with Direct Assessments and a multitask model that predicts sentence-level scores and word-level OK/BAD tags. It provides improved correlations with human judgments and increased robustness to critical errors compared to previous metrics.
Updated Aug 11, 2026
Models3
Model coverage3
MetricScore
EvidenceB
Translation Set1→en COMET22 Ranking
Higher score ranks better on this benchmark.
| 01 | AM Nova ProAmazon | 89.0% | 100.0% | 3 | C | |
| 02 | AMNova LiteAmazon | 88.8% | 50.0% | 3 | C | |
| 03 | AMNova MicroAmazon | 88.7% | 0.0% | 3 | C |
Translation Set1→en COMET22 Score Distribution
A closer view of the leading scores on this benchmark.
Translation Set1→en COMET22 Highlights
The leading models and scores on this benchmark.
What is Translation Set1→en COMET22?
What Translation Set1→en COMET22 measures and how its scores work.
COMET-22 is a neural machine translation evaluation metric that uses an ensemble of two models: a COMET estimator trained with Direct Assessments and a multitask model that predicts sentence-level scores and word-level OK/BAD tags. It provides improved correlations with human judgments and increased robustness to critical errors compared to previous metrics.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Translation Set1→en COMET22
- Modality
- text
- Primary category
- language
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- translation-set1→en-comet22|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Translation Set1→en COMET22.
Which model scores highest on Translation Set1→en COMET22?
Nova Pro is currently ranked first with 89.0%.
What does Translation Set1→en COMET22 measure?
COMET-22 is a neural machine translation evaluation metric that uses an ensemble of two models: a COMET estimator trained with Direct Assessments and a multitask model that predicts sentence-level scores and word-level OK/BAD tags. It provides improved correlations with human judgments and increased robustness to critical errors compared to previous metrics.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
3 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.