language benchmark
MEGA XCOPA Leaderboard
MEGA XCOPA is a multilingual dataset for causal commonsense reasoning in 11 languages.
Updated Sep 5, 2026
Models2
Model coverage2
MetricScore
EvidenceB
MEGA XCOPA Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMI | Score76.60% | Percentile100.00% | Participants2 | EvidenceC | Evaluated |
| Rank02 | ModelMI | Score63.10% | Percentile0.00% | Participants2 | EvidenceC | Evaluated |
MEGA XCOPA Highlights
The leading models and scores on this benchmark.
MEGA XCOPA Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for MEGA XCOPA
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What is MEGA XCOPA?
What MEGA XCOPA measures and how its scores work.
MEGA XCOPA is XCOPA (Cross-lingual Choice of Plausible Alternatives) as part of the MEGA benchmark suite, covering 11 typologically diverse languages, including Eastern Apurímac Quechua and Haitian Creole.
It measures models' ability to select which choice is the effect or cause of a given premise, using Score reported as a ratio.
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
- overall
LLM Stats. 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 76.60%.
What are the top three models on MEGA XCOPA?
The current leaders are Phi-3.5-MoE-instruct (76.60%) and Phi-3.5-mini-instruct (63.10%).
Which MEGA XCOPA model has the lowest official input price?
No matched official input price is currently available.
Which models are fastest among MEGA XCOPA 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.