multimodal benchmark
MTVQA is a multilingual text-centric visual question answering benchmark with human expert annotations across 9 diverse languages, comprising 6,778 question-answer pairs across 2,116 images.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score30.90% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis mtvqa AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Qwen2-VL-72B-Instruct currently leads MTVQA with 30.90%. 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 MTVQA measures and how its scores work.
MTVQA (Multilingual Text-Centric Visual Question Answering) is a multimodal benchmark for multilingual text-centric visual question answering.
MTVQA addresses visual-textual misalignment problems and reports a Score as a ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about MTVQA.
Qwen2-VL-72B-Instruct is currently ranked first with 30.90%.
The current leaders are Qwen2-VL-72B-Instruct (30.90%).
No matched official input price is currently available.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
MTVQA addresses visual-textual misalignment problems and reports a Score as a ratio.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.