language benchmark
MEGA MLQA
MLQA as part of the MEGA (Multilingual Evaluation of Generative AI) benchmark suite. A multi-way aligned extractive QA evaluation benchmark for cross-lingual question answering across 7 languages (English, Arabic, German, Spanish, Hindi, Vietnamese, and Simplified Chinese) with over 12K QA instances in English and 5K in each other language.
Updated Aug 11, 2026
Models2
Model coverage2
MetricScore
EvidenceB
MEGA MLQA Ranking
Higher score ranks better on this benchmark.
| 1 | MI | 65.3% | 100.0% | 2 | C | |
| 2 | MI | 61.7% | 0.0% | 2 | C |
MEGA MLQA Score Distribution
A closer view of the leading scores on this benchmark.
MEGA MLQA Highlights
The leading models and scores on this benchmark.
What is MEGA MLQA?
What MEGA MLQA measures and how its scores work.
MLQA as part of the MEGA (Multilingual Evaluation of Generative AI) benchmark suite. A multi-way aligned extractive QA evaluation benchmark for cross-lingual question answering across 7 languages (English, Arabic, German, Spanish, Hindi, Vietnamese, and Simplified Chinese) with over 12K QA instances in English and 5K in each other language.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- MEGA MLQA
- Modality
- text
- Primary category
- language
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- mega-mlqa|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about MEGA MLQA.
Which model scores highest on MEGA MLQA?
Phi-3.5-MoE-instruct is currently ranked first with 65.3%.
What does MEGA MLQA measure?
MLQA as part of the MEGA (Multilingual Evaluation of Generative AI) benchmark suite. A multi-way aligned extractive QA evaluation benchmark for cross-lingual question answering across 7 languages (English, Arabic, German, Spanish, Hindi, Vietnamese, and Simplified Chinese) with over 12K QA instances in English and 5K in each other language.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
2 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.