language benchmark
MEGA TyDi QA Leaderboard
MEGA TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
Updated Sep 5, 2026
Models2
Model coverage2
MetricScore
EvidenceB
MEGA TyDi QA Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMI | Score67.10% | Percentile100.00% | Participants2 | EvidenceC | Evaluated |
| Rank02 | ModelMI | Score62.20% | Percentile0.00% | Participants2 | EvidenceC | Evaluated |
MEGA TyDi QA Highlights
The leading models and scores on this benchmark.
MEGA TyDi QA Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for MEGA TyDi QA
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What is MEGA TyDi QA?
What MEGA TyDi QA measures and how its scores work.
MEGA TyDi QA is TyDi QA as part of the MEGA benchmark suite, covering Arabic, Bengali, English, Finnish, Indonesian, Japanese, Korean, Russian, Swahili, Telugu, and Thai.
It measures question-answering performance 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 TyDi QA
- 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 TyDi QA.
Which model scores highest on MEGA TyDi QA?
Phi-3.5-MoE-instruct is currently ranked first with 67.10%.
What are the top three models on MEGA TyDi QA?
The current leaders are Phi-3.5-MoE-instruct (67.10%) and Phi-3.5-mini-instruct (62.20%).
Which MEGA TyDi QA model has the lowest official input price?
No matched official input price is currently available.
Which models are fastest among MEGA TyDi QA 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.