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 29, 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
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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