OSWorld 2.0 (Partial)

OSWorld 2.0 (Partial) is a benchmark for how well a model acts as a general-purpose agent. The official description says: "OSWorld 2.0 - Partial score". Below we describe in simple words what benchmarks of this type generally examine.

OSWorld 2.0 (Partial) 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

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: OSWorld 2.0 - Partial score. The partial score aggregates the partial rewards obtained across all tasks.

What is the OSWorld 2.0 (Partial) benchmark?

OSWorld 2.0 (Partial) is a benchmark for how well a model acts as a general-purpose agent. The official description says: 'OSWorld 2.0 - Partial score'. Below we describe in simple words what benchmarks of this type generally examine.

What does OSWorld 2.0 (Partial) test?

OSWorld 2.0 (Partial) 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 OSWorld 2.0 (Partial) 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 OSWorld 2.0 (Partial) 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/osworld-2-partial/scores) or pull it directly from the Open LLM Radar API with any HTTP client.