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language benchmark

CoVoST2

CoVoST 2 is a large-scale multilingual speech translation corpus derived from Common Voice, covering translations from 21 languages into English and from English into 15 languages. The dataset contains 2,880 hours of speech with 78K speakers for speech translation research.

Updated Aug 11, 2026

Models4
Model coverage4
MetricScore
EvidenceB

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  • Distribution
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  • FAQ

CoVoST2 Ranking

Higher score ranks better on this benchmark.

4 rows
Columns

Show columns

01AMNova 2 OmniAmazon40.7%100.0%4CAug 11, 2026
02GOGemini 2.0 FlashGoogle39.2%66.7%4CAug 11, 2026
03GOGemma 4 12BGoogle38.5%33.3%4CAug 11, 2026
04GOGemini 2.0 Flash-LiteGoogle38.4%0.0%4CAug 11, 2026

CoVoST2 Score Distribution

A closer view of the leading scores on this benchmark.

CoVoST2

CoVoST2 Highlights

The leading models and scores on this benchmark.

Rank #1Nova 2 Omni40.7%Rank #2Gemini 2.0 Flash39.2%Rank #3Gemma 4 12B38.5%Rank #4Gemini 2.0 Flash-Lite38.4%

What is CoVoST2?

What CoVoST2 measures and how its scores work.

CoVoST 2 is a large-scale multilingual speech translation corpus derived from Common Voice, covering translations from 21 languages into English and from English into 15 languages. The dataset contains 2,880 hours of speech with 78K speakers for speech translation research.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
CoVoST2
Modality
audio
Primary category
language
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
covost2|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about CoVoST2.

Which model scores highest on CoVoST2?

Nova 2 Omni is currently ranked first with 40.7%.

What does CoVoST2 measure?

CoVoST 2 is a large-scale multilingual speech translation corpus derived from Common Voice, covering translations from 21 languages into English and from English into 15 languages. The dataset contains 2,880 hours of speech with 78K speakers for speech translation research.

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.