RefSpatialBench
RefSpatialBench - referring spatial reasoning benchmark.
Category
Spatial Intelligence
Score type
percent
Scale
100%
higher is better
Models tested
9
What is RefSpatialBench?
RefSpatialBench is a benchmark for understanding where things are in space. The official description says: "RefSpatialBench - referring spatial reasoning benchmark". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
RefSpatialBench 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: RefSpatialBench - referring spatial reasoning benchmark.
Frequently asked questions
What is the RefSpatialBench benchmark?
RefSpatialBench is a benchmark for understanding where things are in space. The official description says: 'RefSpatialBench - referring spatial reasoning benchmark'. Below we describe in simple words what benchmarks of this type generally examine.
What does RefSpatialBench test?
RefSpatialBench 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 RefSpatialBench 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 RefSpatialBench 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/refspatialbench/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 9 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Qwen3.6-27B
Qwen Team (Alibaba Cloud)
|
70%
|
22.04.2026 | Qwen Team (official model card) | Verified |
| 2 |
Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
|
69.9%
|
01.02.2026 | Report | Unverified |
| 3 |
Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
|
69.3%
|
01.02.2026 | Report | Verified |
| 4 |
Qwen3.5-27B
Qwen Team (Alibaba Cloud)
|
67.7%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 5 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
64.3%
|
16.04.2026 | Qwen Team (official model card) | Verified |
| 6 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
63.5%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 7 |
Qwen3.5-4B
Qwen
|
54.6%
|
01.02.2026 | Report | Verified |
| 8 |
LFM3-VL-2B
LiquidAI
|
49.8%
|
— | Report | Verified |
| 9 |
Gemma4-31B-it
Google
|
4.7%
|
22.04.2026 | Qwen Team (official model card - comparison) | Unverified |