language benchmark
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
Higher score ranks better on this benchmark.
| 01 | MI | 76.6% | 100.0% | 2 | C | |
| 02 | MI | 63.1% | 0.0% | 2 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
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.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about MEGA XCOPA.
Phi-3.5-MoE-instruct is currently ranked first with 76.6%.
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.
Yes. Higher values rank better for this benchmark.
2 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.