ERQA
ERQA - embodied/spatial reasoning question answering benchmark.
Category
Spatial Intelligence
Score type
percent
Scale
100%
higher is better
Models tested
13
What is ERQA?
ERQA is a benchmark for understanding where things are in space. The official description says: "ERQA - embodied/spatial reasoning question answering benchmark". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
ERQA 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: ERQA - embodied/spatial reasoning question answering benchmark.
Frequently asked questions
What is the ERQA benchmark?
ERQA is a benchmark for understanding where things are in space. The official description says: 'ERQA - embodied/spatial reasoning question answering benchmark'. Below we describe in simple words what benchmarks of this type generally examine.
What does ERQA test?
ERQA 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 ERQA 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 ERQA 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/erqa/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.
Model scores 13 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Qwen3.8-Flash-Next
Qwen Team (Alibaba)
|
72.3%
|
26.08.2026 | Report | Verified |
| 2 |
Qwen3.8-27B
Qwen Team (Alibaba Cloud)
|
65.5%
|
18.08.2026 | Report | Verified |
| 3 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
64.8%
|
01.02.2026 | Report | Unverified |
| 4 |
Qwen3.6-27B
Qwen Team (Alibaba Cloud)
|
62.5%
|
22.04.2026 | Qwen Team (official model card) | Verified |
| 5 |
Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
|
62%
|
01.02.2026 | Report | Verified |
| 6 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
61.8%
|
22.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 7 |
Qwen3.5-27B
Qwen Team (Alibaba Cloud)
|
60.5%
|
22.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 8 |
Gemma4-31B-it
Google
|
57.5%
|
22.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 9 |
Qwen3.5-4B
Qwen
|
54%
|
01.02.2026 | Report | Verified |
| 10 |
Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
|
52.5%
|
01.02.2026 | Report | Unverified |
| 11 |
LFM3-VL-2B
LiquidAI
|
48.2%
|
— | Report | Verified |
| 12 |
d1-2B
LiquidAI
|
45.8%
|
— | LiquidAI/d1-2B model card (performance table) | Unverified |
| 13 |
LFM3-Omni-2B
LiquidAI
|
38.5%
|
— | Report | Verified |