spatial reasoning benchmark
RealWorldQA
RealWorldQA is a benchmark designed to evaluate basic real-world spatial understanding capabilities of multimodal models. The initial release consists of over 700 anonymized images taken from vehicles and other real-world scenarios, each accompanied by a question and easily verifiable answer. Released by xAI as part of their Grok-1.5 Vision preview to test models' ability to understand natural scenes and spatial relationships in everyday visual contexts.
Updated Aug 11, 2026
Models26
Model coverage26
MetricScore
EvidenceB
RealWorldQA Ranking
Higher score ranks better on this benchmark.
RealWorldQA Score Distribution
A closer view of the leading scores on this benchmark.
RealWorldQA Highlights
The leading models and scores on this benchmark.
What is RealWorldQA?
What RealWorldQA measures and how its scores work.
RealWorldQA is a benchmark designed to evaluate basic real-world spatial understanding capabilities of multimodal models. The initial release consists of over 700 anonymized images taken from vehicles and other real-world scenarios, each accompanied by a question and easily verifiable answer. Released by xAI as part of their Grok-1.5 Vision preview to test models' ability to understand natural scenes and spatial relationships in everyday visual contexts.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- RealWorldQA
- Modality
- multimodal
- Primary category
- spatial reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- realworldqa|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about RealWorldQA.
Which model scores highest on RealWorldQA?
Qwen3.8 Max is currently ranked first with 88.0%.
What does RealWorldQA measure?
RealWorldQA is a benchmark designed to evaluate basic real-world spatial understanding capabilities of multimodal models. The initial release consists of over 700 anonymized images taken from vehicles and other real-world scenarios, each accompanied by a question and easily verifiable answer. Released by xAI as part of their Grok-1.5 Vision preview to test models' ability to understand natural scenes and spatial relationships in everyday visual contexts.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
26 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.