RecreationBench
RecreationBench - in-house long-horizon application-recreation benchmark evaluating hybrid-agent capabilities across five platforms: desktop (Ubuntu, macOS, Windows), mobile (Android), and web.
Category
General Agents
Score type
percent
Scale
100%
higher is better
Models tested
3
What is RecreationBench?
RecreationBench is a benchmark for how well a model acts as a general-purpose agent. The official description says: "RecreationBench - in-house long-horizon application-recreation benchmark evaluating hybrid-agent capabilities across five platforms: desktop (Ubuntu, macOS, Windows), mobile (Android), and web". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
RecreationBench measures how well a model acts as a general-purpose agent. In detail, it checks:
- solving long, multi-step tasks with many intermediate decisions
- using tools correctly (browsers, terminals, APIs, calendars, other apps)
- adapting when something unexpected happens mid-task
- staying on goal over long task horizons instead of wandering off
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: RecreationBench - in-house long-horizon application-recreation benchmark evaluating hybrid-agent capabilities across five platforms: desktop (Ubuntu, macOS, Windows), mobile (Android), and web.
Frequently asked questions
What is the RecreationBench benchmark?
RecreationBench is a benchmark for how well a model acts as a general-purpose agent. The official description says: 'RecreationBench - in-house long-horizon application-recreation benchmark evaluating hybrid-agent capabilities across five platforms: desktop (Ubuntu, macOS, Windows), mobile (Android), and web'. Below we describe in simple words what benchmarks of this type generally examine.
What does RecreationBench test?
RecreationBench mainly measures how well a model acts as a general-purpose agent. Concretely, it checks: solving long, multi-step tasks with many intermediate decisions; using tools correctly (browsers, terminals, APIs, calendars, other apps); adapting when something unexpected happens mid-task.
How is the RecreationBench 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 RecreationBench 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/recreationbench/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 3 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Qwen3.8-Flash-Next
Qwen Team (Alibaba)
|
49.9%
|
26.08.2026 | Report | Verified |
| 2 |
Qwen3.8-27B
Qwen Team (Alibaba Cloud)
|
47.1%
|
18.08.2026 | Report | Verified |
| 3 |
Qwen3.6-27B
Qwen Team (Alibaba Cloud)
|
29.8%
|
18.08.2026 | Report | Unverified |