multimodal benchmark
WorldVQA Leaderboard
WorldVQA is a benchmark designed to evaluate atomic vision-centric world knowledge. It assesses models' ability to understand and reason about visual elements representing real-world knowledge.
Updated Aug 17, 2026
Models5
Model coverage5
MetricScore
EvidenceB
WorldVQA Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score61.1% | Percentile100.0% | Participants5 | EvidenceC | Evaluated |
| Rank02 | ModelBY | Score53.0% | Percentile75.0% | Participants5 | EvidenceC | Evaluated |
| Rank03 | ModelMA | Score51.0% | Percentile50.0% | Participants5 | EvidenceC | Evaluated |
| Rank04 | ModelBY | Score48.6% | Percentile25.0% | Participants5 | EvidenceC | Evaluated |
| Rank05 | ModelMA | Score46.3% | Percentile0.0% | Participants5 | EvidenceC | Evaluated |
WorldVQA Highlights
The leading models and scores on this benchmark.
WorldVQA Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for WorldVQA
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis worldvqa 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.
- 01ACQwen3.7-PlusAlibaba Cloud / Qwen Team
- Score
- 61.1%
- Price
- $0.50 input / $3.0 output per 1M tokens
Strengths
- Ranks #1 of 5 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures WorldVQA, not total model capability
- 02BYSeed 2.1 ProByteDance
- Score
- 53.0%
Strengths
- Ranks #2 of 5 compared models
- 75th percentile on this benchmark
- C evidence result
Considerations
- This result measures WorldVQA, not total model capability
- 03MAKimi K3Moonshot AI
- Score
- 51.0%
- Price
- $3.0 input / $15 output per 1M tokens
- Speed
- Up to 26 tok/s via Fireworks
Strengths
- Ranks #3 of 5 compared models
- 50th percentile on this benchmark
- C evidence result
Considerations
- This result measures WorldVQA, not total model capability
- 04BYSeed 2.1 TurboByteDance
- Score
- 48.6%
Strengths
- Ranks #4 of 5 compared models
- 25th percentile on this benchmark
- C evidence result
Considerations
- This result measures WorldVQA, not total model capability
- 05MAKimi K2.5Moonshot AI
- Score
- 46.3%
- Price
- $0.60 input / $3.0 output per 1M tokens
Strengths
- Ranks #5 of 5 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures WorldVQA, not total model capability
Selection summary
Best AI Models for WorldVQA
Qwen3.7-Plus currently leads WorldVQA with 61.1%. 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 WorldVQA?
What WorldVQA measures and how its scores work.
WorldVQA is a benchmark designed to evaluate atomic vision-centric world knowledge. It assesses models' ability to understand and reason about visual elements representing real-world knowledge.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- WorldVQA
- Modality
- multimodal
- Primary category
- multimodal
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- worldvqa|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about WorldVQA.
Which model scores highest on WorldVQA?
Qwen3.7-Plus is currently ranked first with 61.1%.
What does WorldVQA measure?
WorldVQA is a benchmark designed to evaluate atomic vision-centric world knowledge. It assesses models' ability to understand and reason about visual elements representing real-world knowledge.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
5 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.