language benchmark
Translation Set1→en COMET22 Leaderboard
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 17, 2026
Models3
Model coverage3
MetricScore
EvidenceB
Translation Set1→en COMET22 Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAM Nova ProAmazon | Score89.0% | Percentile100.0% | Participants3 | EvidenceC | Evaluated |
| Rank02 | ModelAMNova LiteAmazon | Score88.8% | Percentile50.0% | Participants3 | EvidenceC | Evaluated |
| Rank03 | ModelAMNova MicroAmazon | Score88.7% | Percentile0.0% | Participants3 | EvidenceC | Evaluated |
Translation Set1→en COMET22 Highlights
The leading models and scores on this benchmark.
Translation Set1→en COMET22 Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for Translation Set1→en COMET22
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis translation set1→en comet22 AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01AMNova ProAmazon
- Score
- 89.0%
- Price
- $0.80 input / $3.2 output per 1M tokens
- Speed
- Up to 100 tok/s via Bedrock
Strengths
- Ranks #1 of 3 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures Translation Set1→en COMET22, not total model capability
- 02AMNova LiteAmazon
- Score
- 88.8%
- Price
- $0.06 input / $0.24 output per 1M tokens
- Speed
- Up to 100 tok/s via Bedrock
Strengths
- Ranks #2 of 3 compared models
- 50th percentile on this benchmark
- C evidence result
Considerations
- This result measures Translation Set1→en COMET22, not total model capability
- 03AMNova MicroAmazon
- Score
- 88.7%
- Price
- $0.04 input / $0.14 output per 1M tokens
- Speed
- Up to 100 tok/s via Bedrock
Strengths
- Ranks #3 of 3 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures Translation Set1→en COMET22, not total model capability
Selection summary
Best AI Models for Translation Set1→en COMET22
Nova Pro currently leads Translation Set1→en COMET22 with 89.0%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
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.