Multimodal · Benchmark profile
A visual counting benchmark that tests whether a model can count objects and entities reliably in complex scenes.
Data verified 27 Jul 2026 · Methodology 1.8.0
Benchmark score on CountBench
Qwen3.6 27B leads at 97.8%, followed by LFM2.5-VL-450M (73.3%).
2 ranked models · higher is better · labels show rank and score
| Model | Rank | Provider | Score |
|---|---|---|---|
| Qwen3.6 27B | #1 | Alibaba Cloud | 97.8% |
| LFM2.5-VL-450M | #2 | LiquidAI | 73.3% |
One best score per model · higher is better
| Rank | Model | Provider | License | Evidence use | Score |
|---|---|---|---|---|---|
| #1 | Qwen3.6 27B qwen3.6-27b | Alibaba Cloud | open | Estimated reference | 97.8% |
| #2 | LFM2.5-VL-450M lfm2-5-vl-450m | LiquidAI | open | Estimated reference | 73.3% |
About CountBench
A visual counting benchmark that tests whether a model can count objects and entities reliably in complex scenes. Results stay tied to the exact model variant and evaluation system. Multiple systems for the same model use the best published score on this page; overall Lumina scoring uses the median of ranking-eligible rows.
Open benchmark source ↗FAQ
A visual counting benchmark that tests whether a model can count objects and entities reliably in complex scenes.
Qwen3.6 27B by Alibaba Cloud currently leads with 97.8%.
2 models in the LuminaBench cohort have a qualifying score on this benchmark.
No. This benchmark is display-only and does not enter the overall Lumina composite.
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