language benchmark
FLEURS
Few-shot Learning Evaluation of Universal Representations of Speech - a parallel speech dataset in 102 languages built on FLoRes-101 with approximately 12 hours of speech supervision per language for tasks including ASR, speech language identification, translation and retrieval. Scores are shown as speech recognition accuracy (1 - word error rate), so higher is better.
Updated Aug 11, 2026
Models6
Model coverage6
MetricScore
EvidenceB
FLEURS Ranking
Higher score ranks better on this benchmark.
| 1 | AC | 95.9% | 100.0% | 6 | C | |
| 2 | GO | 93.6% | 80.0% | 6 | B | |
| 3 | GO | 93.3% | 60.0% | 6 | C | |
| 4 | GO | 93.1% | 40.0% | 6 | C | |
| 5 | GO | 90.4% | 20.0% | 6 | C | |
| 6 | GO | 86.4% | 0.0% | 6 | C |
FLEURS Score Distribution
A closer view of the leading scores on this benchmark.
FLEURS Highlights
The leading models and scores on this benchmark.
What is FLEURS?
What FLEURS measures and how its scores work.
Few-shot Learning Evaluation of Universal Representations of Speech - a parallel speech dataset in 102 languages built on FLoRes-101 with approximately 12 hours of speech supervision per language for tasks including ASR, speech language identification, translation and retrieval. Scores are shown as speech recognition accuracy (1 - word error rate), so higher is better.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- FLEURS
- Modality
- audio
- Primary category
- language
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- fleurs|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about FLEURS.
Which model scores highest on FLEURS?
Qwen2.5-Omni-7B is currently ranked first with 95.9%.
What does FLEURS measure?
Few-shot Learning Evaluation of Universal Representations of Speech - a parallel speech dataset in 102 languages built on FLoRes-101 with approximately 12 hours of speech supervision per language for tasks including ASR, speech language identification, translation and retrieval. Scores are shown as speech recognition accuracy (1 - word error rate), so higher is better.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
6 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.