Benchmark profile
CountBench
A visual counting benchmark that tests whether a model can count objects and entities reliably in complex scenes.
Data verifiedBenchmark score on CountBench — July 23, 2026
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
qwen3-6-27b
LFM2.5-VL-450M
LiquidAI
lfm2-5-vl-450m
Benchmark score table (2 models)
ScoreAbout CountBench
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.
BenchLM freshness & provenance
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.
FAQ
What does CountBench measure?
A visual counting benchmark that tests whether a model can count objects and entities reliably in complex scenes.
Which model scores highest on CountBench?
Qwen3.6-27B by Alibaba currently leads with a score of 97.8% on CountBench.
How many models are evaluated on CountBench?
2 AI models have been evaluated on CountBench on BenchLM.
Compare Top Models on CountBench
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