reasoning benchmark
MEGA XCOPA Leaderboard
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 17, 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 | Score76.6% | Percentile100.0% | Participants2 | EvidenceC | Evaluated |
| Rank02 | ModelMI | Score63.1% | Percentile0.0% | 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.
Ranking basisThis mega xcopa AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01MIPhi-3.5-MoE-instructMicrosoft
- Score
- 76.6%
Strengths
- Ranks #1 of 2 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures MEGA XCOPA, not total model capability
- 02MIPhi-3.5-mini-instructMicrosoft
- Score
- 63.1%
- Speed
- Up to 23 tok/s via Azure
Strengths
- Ranks #2 of 2 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures MEGA XCOPA, not total model capability
Selection summary
Best AI Models for MEGA XCOPA
Phi-3.5-MoE-instruct currently leads MEGA XCOPA with 76.6%. 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.
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
- reasoning
- 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.