Spatial Intelligence Benchmark

CountBench

CountBench - visual object counting benchmark.

Category

Spatial Intelligence

Score type

percent

Scale

100%

higher is better

Models tested

8

What is CountBench?

CountBench is a benchmark for understanding where things are in space. The official description says: "CountBench - visual object counting benchmark". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

CountBench measures understanding where things are in space. In detail, it checks:

  • locating and pointing to regions in images
  • reasoning about positions, counts and relations between objects
  • 3D-aware questions such as depth, perspective and room layout
  • grounded perception tasks that must stay anchored to pixel locations

How to read the score

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Official methodology note: CountBench - visual object counting benchmark.

Frequently asked questions

What is the CountBench benchmark?

CountBench is a benchmark for understanding where things are in space. The official description says: 'CountBench - visual object counting benchmark'. Below we describe in simple words what benchmarks of this type generally examine.

What does CountBench test?

CountBench mainly measures understanding where things are in space. Concretely, it checks: locating and pointing to regions in images; reasoning about positions, counts and relations between objects; 3D-aware questions such as depth, perspective and room layout.

How is the CountBench score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get CountBench data and scores?

The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/countbench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 8 models tested

Reset
# Model Score Date Source Verified
1 Qwen3.6-27B
Qwen Team (Alibaba Cloud)
97.8%
22.04.2026 Qwen Team (official model card) Verified
2 Qwen3.5-27B
Qwen Team (Alibaba Cloud)
97.8%
22.04.2026 Qwen Team (official model card - comparison) Unverified
3 Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
97.8%
01.02.2026 Report Unverified
4 Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
97%
01.02.2026 Report Verified
5 Qwen3.5-4B
Qwen
96.3%
01.02.2026 Report Verified
6 Gemma4-31B-it
Google
96.1%
22.04.2026 Qwen Team (official model card - comparison) Unverified
7 Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
96.1%
22.04.2026 Qwen Team (official model card - comparison) Unverified
8 Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
93.7%
01.02.2026 Report Unverified

What now?

You've seen how models score on CountBench — here's what to do next.