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language benchmark

MEGA XCOPA

XCOPA (Cross-lingual Choice of Plausible Alternatives) as part of the MEGA benchmark suite. A typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, including resource-poor languages like Eastern Apurímac Quechua and Haitian Creole. Requires models to select which choice is the effect or cause of a given premise.

Updated Aug 11, 2026

Models2
Model coverage2
MetricScore
EvidenceB

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  • FAQ

MEGA XCOPA Ranking

Higher score ranks better on this benchmark.

2 rows
Columns

Show columns

01MIPhi-3.5-MoE-instructMicrosoft76.6%100.0%2CAug 11, 2026
02MIPhi-3.5-mini-instructMicrosoft63.1%0.0%2CAug 11, 2026

MEGA XCOPA Score Distribution

A closer view of the leading scores on this benchmark.

MEGA XCOPA

MEGA XCOPA Highlights

The leading models and scores on this benchmark.

Rank #1Phi-3.5-MoE-instruct76.6%Rank #2Phi-3.5-mini-instruct63.1%

What is MEGA XCOPA?

What MEGA XCOPA measures and how its scores work.

XCOPA (Cross-lingual Choice of Plausible Alternatives) as part of the MEGA benchmark suite. A typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, including resource-poor languages like Eastern Apurímac Quechua and Haitian Creole. Requires models to select which choice is the effect or cause of a given premise.

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

Family
MEGA XCOPA
Modality
text
Primary category
language
Score direction
higher
LLMBoard eligible
No
Evaluation key
mega-xcopa|llm-stats-current

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

FAQ

Common questions about MEGA XCOPA.

Which model scores highest on MEGA XCOPA?

Phi-3.5-MoE-instruct is currently ranked first with 76.6%.

What does MEGA XCOPA measure?

XCOPA (Cross-lingual Choice of Plausible Alternatives) as part of the MEGA benchmark suite. A typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, including resource-poor languages like Eastern Apurímac Quechua and Haitian Creole. Requires models to select which choice is the effect or cause of a given premise.

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.