pretty_name: OpenHome
license: cc-by-nc-4.0
task_categories:
- video-text-to-text
- multiple-choice
language:
- en
tags:
- video-understanding
- smart-home
- benchmark
- video-qa
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: test
path: openhome.csv
OpenHome: Can Video Models Understand What Really Happens on a Smart-Home Camera?
OpenHome is a multiple-choice video QA benchmark built from real smart-home camera clips. The clips are short, fixed-viewpoint, often recorded at night or in infrared, and come with no narration or subtitles, so models must reason from sparse visual signals alone. The benchmark is organised into three levels that move from Perception through Reasoning to Insight: reading details of a subject and its interactions and deciding what the homeowner should do about it.
Dataset at a glance
| Questions | 2,254 (four options, A–D) |
| Clips | 1,118 real-world clips, 5.97 hours total, mean 19.2 s |
| Conditions | ~50% day / ~47% night (22% infrared), mostly outdoor |
| Subjects | animals, vehicles, people and natural phenomena |
| Split | test only |
The three levels cover 10 tasks:
| Category | Tasks | Questions |
|---|---|---|
| Perception: what appears in the clip | Attribute, Recognition, Change, Count | 989 |
| Reasoning: how it relates in cause, space and time | Causality, Spatial, Temporal | 721 |
| Insight: what the subject is up to and what the homeowner should do | Intent, Evidence, Response | 544 |
Every question is answerable from the video frames alone. Questions were drafted by VLMs, hardened against text-only shortcuts, and verified by human annotators against the clip. Correct-answer positions are balanced across A–D (best constant-answer baseline: 26.9%).
Fields
| Column | Description |
|---|---|
question_id |
Unique question ID (<video_id>_q<n>) |
video_url |
Link to the source clip |
category |
Perception, Reasoning or Insight |
type |
One of the 10 tasks |
question |
Question text |
option_A … option_D |
Answer options |
ground_truth |
Correct option letter (A–D) |
from datasets import load_dataset
ds = load_dataset("wyzelabs/OpenHome", split="test")
Model performance
Category accuracy (%) of selected closed and open-weight models. Closed-source models lead open-weight models on every level.
| # | Model | Perception | Reasoning | Insight | Overall |
|---|---|---|---|---|---|
| 1 | Gemini 4 Argon | 81.6 | 74.8 | 89.2 | 81.2 |
| 2 | GPT-6 Astra | 76.4 | 68.9 | 86.9 | 76.6 |
| 3 | Gemini 3.8 Flash | 70.9 | 63.1 | 79.0 | 70.4 |
| 4 | Muse Wonder 1.0 | 69.9 | 62.1 | 73.7 | 68.3 |
| 5 | Gemini 3.1 Pro | 65.0 | 59.8 | 66.7 | 63.7 |
| 6 | Gemini 2.5 Pro | 63.5 | 52.3 | 57.9 | 58.6 |
| 7 | Muse-Glimmer-30B | 65.3 | 52.4 | 51.7 | 57.9 |
| 8 | Grok 4.7 | 59.6 | 53.5 | 57.7 | 57.2 |
| 9 | Gemini 3.5 Flash-Lite | 60.4 | 48.8 | 54.0 | 55.1 |
| 10 | Gemma 4 31B-it | 62.1 | 51.5 | 45.4 | 54.7 |
| 11 | GPT-4o | 56.8 | 47.3 | 55.7 | 53.5 |
| 12 | Qwen3.5-397B-A17B | 56.5 | 47.2 | 50.2 | 52.0 |
| 13 | GPT-5 mini | 59.2 | 49.1 | 42.1 | 51.8 |
| 14 | Qwen3.8-Flash-Next | 55.9 | 49.9 | 46.5 | 51.7 |
| 15 | Qwen3.5-27B | 53.5 | 45.6 | 46.0 | 49.2 |
| 16 | MiMo-V2.6-Pro-MOPD | 50.6 | 41.7 | 46.0 | 46.6 |
| 17 | MiMo-V2.6-Flash-MOPD | 47.3 | 40.6 | 39.0 | 43.2 |
Evaluation protocol: frames sampled at 1 fps (max 64), ~200K pixels per frame, no audio or metadata, temperature 0 and minimal reasoning. Full results for all 32 models are in the technical report.
Citation
@techreport{openhome2026,
title = {OpenHome: Can Video Models Understand What Really Happens on a Smart-Home Camera?},
author = {Wyze Labs},
year = {2026},
institution = {Wyze Labs}
}