A general visual question-answering benchmark used in provider tables for real-image reasoning quality.
BenchLM mirrors the published score view for MStar. Qwen3.6-27B leads the public snapshot at 81.4%. BenchLM does not use these results to rank models overall.
Year
2026
Tasks
Real-image visual QA
Format
Image-grounded QA
Difficulty
General visual reasoning
MStar sits between broad multimodal reasoning and grounded VQA. It is useful for checking whether a model can answer real-image questions without the stronger domain structure of office or academic benchmarks.
Version
MStar 2026
Refresh cadence
Quarterly
Staleness state
Current
Question availability
Public benchmark set
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
A general visual question-answering benchmark used in provider tables for real-image reasoning quality.
Qwen3.6-27B by Alibaba currently leads with a score of 81.4% on MStar.
1 AI models have been evaluated on MStar on BenchLM.
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