General Agents Benchmark

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.

Model scores 3 models tested

Reset
# 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

What now?

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