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language benchmark

MEGA MLQA Leaderboard

MEGA MLQA is a multilingual extractive question-answering benchmark covering seven languages.

Updated Sep 5, 2026

Models2
Model coverage2
MetricScore
EvidenceB

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  • Ranking
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  • FAQ

MEGA MLQA Ranking

Higher score ranks better on this benchmark.

2 rows
Columns

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Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelMIPhi-3.5-MoE-instructMicrosoftScore65.30%Percentile100.00%Participants2EvidenceCEvaluatedSep 8, 2026
Rank02ModelMIPhi-3.5-mini-instructMicrosoftScore61.70%Percentile0.00%Participants2EvidenceCEvaluatedSep 8, 2026

MEGA MLQA Highlights

The leading models and scores on this benchmark.

Rank #1Phi-3.5-MoE-instruct65.30%Rank #2Phi-3.5-mini-instruct61.70%

MEGA MLQA Score Distribution

A closer view of the leading scores on this benchmark.

MEGA MLQA

The Top AI Models for MEGA MLQA

The first five results on this benchmark, with official price and output speed added where the model identity can be matched.

What is MEGA MLQA?

What MEGA MLQA measures and how its scores work.

MEGA MLQA is part of the MEGA (Multilingual Evaluation of Generative AI) benchmark suite and contains a multi-way aligned extractive QA evaluation dataset with over 12K QA instances in English and 5K in each other language.

It measures cross-lingual question answering across English, Arabic, German, Spanish, Hindi, Vietnamese, and Simplified Chinese using the Score metric as a ratio.

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
overall

LLM Stats. 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 65.30%.

What are the top three models on MEGA MLQA?

The current leaders are Phi-3.5-MoE-instruct (65.30%) and Phi-3.5-mini-instruct (61.70%).

Which MEGA MLQA model has the lowest official input price?

No matched official input price is currently available.

Which models are fastest among MEGA MLQA results?

The fastest matched records are Phi-3.5-mini-instruct (23.00 tok/s via Azure).

Does the highest score result prove overall model quality?

No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.

What does MEGA MLQA measure?

It measures cross-lingual question answering across English, Arabic, German, Spanish, Hindi, Vietnamese, and Simplified Chinese using the Score metric as a ratio.

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.

Ranking basisThis mega mlqa AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    MI
    Phi-3.5-MoE-instructMicrosoft
    Score
    65.30%

    Strengths

    • Ranks #1 of 2 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures MEGA MLQA, not total model capability
  2. 02
    MI
    Phi-3.5-mini-instructMicrosoft
    Score
    61.70%
    Speed
    Up to 23.00 tok/s via Azure

    Strengths

    • Ranks #2 of 2 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures MEGA MLQA, not total model capability

Selection summary

Best AI Models for MEGA MLQA

Phi-3.5-MoE-instruct currently leads MEGA MLQA with 65.30%. 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.

Benchmark rank #1Phi-3.5-MoE-instruct65.30%
Benchmark rank #2Phi-3.5-mini-instruct61.70% · Up to 23.00 tok/s via Azure