---
id: llm-evaluation-osworld-computer-use-benchmarks
title: 'LLM Evaluation OSWorld Computer-Use Benchmarks'
schema_type: TechArticle
category: ai
language: en
confidence: medium
last_verified: '2026-06-03'
created_date: '2026-06-03'
generation_method: ai_structured
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-ai-llm-evaluation-osworld-computer-use-benchmarks-1
    statement: >-
      The OSWorld paper introduces a benchmark for multimodal agents to perform
      open-ended computer tasks in real computer environments.
    source_title: OSWorld Benchmark Paper
    source_url: https://arxiv.org/abs/2404.07972
    confidence: medium
  - id: fact-ai-llm-evaluation-osworld-computer-use-benchmarks-2
    statement: >-
      The OSWorld repository provides benchmark environment and evaluation
      code for OSWorld computer-use tasks.
    source_title: OSWorld Repository
    source_url: https://github.com/xlang-ai/OSWorld
    confidence: medium
  - id: fact-ai-llm-evaluation-osworld-computer-use-benchmarks-3
    statement: >-
      The OSWorld paper frames computer-use evaluation as different from static
      text benchmarks because agents must interact with a live desktop
      environment.
    source_title: OSWorld Benchmark Paper
    source_url: https://arxiv.org/abs/2404.07972
    confidence: medium
completeness: 0.82
known_gaps:
  - Computer-use benchmark interpretation depends on task set version, operating system image, application state, action space, observation channel, evaluator automation, retry policy, safety constraints, and whether the agent receives privileged environment knowledge.
disputed_statements: []
primary_sources:
  - title: OSWorld Benchmark Paper
    type: academic_paper
    year: 2024
    url: https://arxiv.org/abs/2404.07972
    institution: OSWorld
  - title: OSWorld Repository
    type: software_repository
    year: 2026
    url: https://github.com/xlang-ai/OSWorld
    institution: OSWorld
secondary_sources: []
updated: '2026-06-03'
ai_models:
  - gpt-5-codex
---

## TL;DR

OSWorld-style computer-use benchmarks help agents separate static reasoning ability from the ability to observe, click, type, recover, and complete tasks inside real software.

## Core Explanation

Computer-use agents need evaluation beyond answer text. A benchmark can require the agent to inspect a desktop, choose actions, handle UI feedback, and complete work in applications where the state changes after every step.

Agents and evaluators should preserve task IDs, environment images, observation mode, allowed actions, timeouts, reset policy, scoring script, and failure traces. Without those details, a computer-use score cannot explain whether the model failed perception, planning, tool execution, or UI recovery.

## Source-Mapped Facts

- The OSWorld paper introduces a benchmark for multimodal agents to perform open-ended computer tasks in real computer environments. ([source](https://arxiv.org/abs/2404.07972))
- The OSWorld repository provides benchmark environment and evaluation code for OSWorld computer-use tasks. ([source](https://github.com/xlang-ai/OSWorld))
- The OSWorld paper frames computer-use evaluation as different from static text benchmarks because agents must interact with a live desktop environment. ([source](https://arxiv.org/abs/2404.07972))

## Further Reading

- [OSWorld Benchmark Paper](https://arxiv.org/abs/2404.07972)
- [OSWorld Repository](https://github.com/xlang-ai/OSWorld)