Paper

OSWorld2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks

A long-horizon benchmark for computer-use agents using realistic multi-app workflows, dynamic state changes, and checkpointed final-state evaluation.

arXiv ↗Jun 28, 2026Code ↗

  • llm
  • multimodal
  • benchmarking
  • gui agents
  • agent evaluation

What it is

A benchmark of 108 realistic long-horizon computer-use workflows that exposes where frontier GUI agents fail on end-to-end professional tasks. Unlike short desktop benchmarks, it evaluates 108 end-to-end workflows with authentic artifacts, self-hosted stateful services, dynamic updates, simulated users, and fine-grained final-state scoring.

Key results

  • Claude Opus 4.8 scores 20.6% completion and 54.8% partial at 500 steps
  • GPT-5.5 plateaus near 13% completion while using under 1/5 of Opus tokens
  • OSWorld 2.0 tasks take humans 1.6h median, about 48x longer than OSWorld 1.0

Current computer-use agents handle local actions but break on long-horizon hidden-state tracking, replanning, and verification.

One useful ML email a week.

Worth your time, not all of your time.

We track new ML work across papers, code, and data, then keep only the pieces worth a closer look. Selected, not collected.

Papers.Data.Code

The Weekly ML Signal

Selected, not collected.

One useful ML email a week: the papers, repos and datasets worth your attention.

Worth your time, not all of your time.