A visual counting benchmark that tests whether a model can count objects and entities reliably in complex scenes.
BenchLM mirrors the published score view for CountBench. Qwen3.6-27B leads the public snapshot at 97.8% , followed by LFM2.5-VL-450M (73.3%). BenchLM does not use these results to rank models overall.
Qwen3.6-27B
Alibaba
LFM2.5-VL-450M
LiquidAI
Year
2026
Tasks
Visual counting tasks
Format
Image-grounded counting
Difficulty
Fine-grained visual perception
Counting failures are a common multimodal weakness even in otherwise strong models. CountBench isolates that skill and makes it easy to compare raw perception accuracy across models.
Version
CountBench 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 visual counting benchmark that tests whether a model can count objects and entities reliably in complex scenes.
Qwen3.6-27B by Alibaba currently leads with a score of 97.8% on CountBench.
2 AI models have been evaluated on CountBench on BenchLM.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.