Benchmark Almanac

OSWorld 2.0 (Aug 2026)

Claude Opus 4.8 37.6% leads since August 2026
Claude Fable 5.1 77.9% leads since September 2026
Only in system cards

Agents complete 108 long-horizon workflows across 31 self-hosted websites and desktop apps, averaging 318 tool calls. Scored by binary completion and checkpoint partial credit.

Patch release on the same 108 tasks: 14 changed to prevent reward hacking and 31 modified.

Made by
XLANG Lab, HKU
Released
August 2026
Kind
Desktop apps
Level
University
Scored
Checked automatically
Answers
Agent task
Tasks
All public
Strongest model tested
Claude Fable 5.1 #7 on the capability index
Last in a system card
September 2026
Last model added
September 2026
Scores refreshed
November 12th, 2026

In the authors’ words

Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of frontier agents. We introduce OSWorld 2.0, a benchmark of 108 long-horizon computer-use workflows across everyday and professional tasks, designed to capture complex and challenging real-world phenomena. Each task represents a realistic end-to-end workflow that takes human users a median of about 1.6 hours to complete and requires an average of 318 tool calls with Claude Opus 4.7 using maximum thinking, compared with about 30 in OSWorld 1.0. OSWorld 2.0 targets challenge phenomena that are common in real workflows yet underrepresented in prior benchmarks, spanning interaction-design challenges such as streaming interaction and dynamic environments, as well as agent-pattern challenges such as cross-source reasoning, implicit-state inference, and visual-spatial precision. Tasks are grounded in authentic input artifacts and cross-referenced against realistic stateful user profile data, and include separate safety reports auditing safety-sensitive execution. Under our primary binary-completion metric at 500 steps, Claude Opus 4.8 with maximum thinking and batched tool calls scores best but still completes only 20.6% of tasks at a 54.8% partial score; GPT-5.5 is far more token-efficient yet plateaus near 13%. These results show that current agents are still far from professional-level computer use: rather than stumbling on basic GUI control or coding, they lose track of constraints, miss information that arrives mid-task, guess rather than ask the user, and skip verification, struggling most when a task hinges on hidden state they must recover.

Abstract of OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks (arXiv:2606.29537, CC0)
1
Model scored in lab cards
1
System card reports it, from 1 lab

Claude Fable 5.1 leads with 77.9%

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

Claude Opus 4.8 leads with 37.6% across all independent runs

Best score per model across the official leaderboard, Epoch AI’s own runs and any board Epoch AI copied. Each bar and row names its source.

Claude Fable 5.1 leads with 77.9%, claims included

Best score per model from every source, including labs’ own numbers and claims Epoch AI collected from reports and papers. Labs pick their own settings, so a claim can beat an independent run of the same model.

  1. 1 Claude Fable 5.1 77.9%

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 Claude Fable 5.1 77.9% Sep 2026 Lab model card
#ModelScoreSettingDateSource
1 Claude Opus 4.8 37.6% max (thinking disabled), 450 steps, Darwinx May 2026 Official leaderboard
2 Claude Opus 5 31.4% max, batch tool Jul 2026 Official leaderboard
3 GPT-5.6 Sol 27.3% max, batch tool Jul 2026 Official leaderboard
#ModelScoreSettingDateSource
1 Claude Fable 5.1 77.9% Sep 2026 Lab model card
2 Claude Opus 4.8 37.6% max (thinking disabled), 450 steps, Darwinx May 2026 Official leaderboard
3 Claude Opus 5 31.4% max, batch tool Jul 2026 Official leaderboard
4 GPT-5.6 Sol 27.3% max, batch tool Jul 2026 Official leaderboard

The benchmark's own leaderboard has 3 models. Switch the scores above to rank them together.

Number one over time Number one over time 2 models have held number one with claims included since its release in August 2026

Lab claims count from the day the card came out, independent runs from the model’s release.

Anthropic
Own leaderboard Replaced by 2.1, Sep 2026

Source: Official OSWorld 2.0 (Aug 2026) leaderboard

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

On the benchmark’s own leaderboard Reported by the lab in its model card Best so far
0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Fable 5.1 Claude Opus 4.8 0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Claude Fable 5.1 Claude Opus 4.8

Sources: Official OSWorld 2.0 (Aug 2026) leaderboard and labs’ model cards

Labs report it only in system cards

We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.

One system card reports it

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
Sep 1, 2026 Claude Fable 5.1 system card 77.9 OSWorld 2.0 (August 2026 task release with fixes), partial score, max effort, production safeguards on; strict pass rate 41.7% (Table 8.1.A, 8.14.3) Capabilities

Versions

We switch the top row to each new version when it comes out. Open it for one row per version. An old version stays in play while people still test it, so whoever first takes it past 85% gets credit for closing it out.

Qwen Anthropic Moonshot AI OpenAI
OSWorld 2.1 14 leaders
OSWorld Not tested since Feb 2026
OSWorld-Verified Passed 85% in Jun 2026, by Claude Fable 5
OSWorld 2.0 Replaced by 2.0 Aug, Aug 2026
OSWorld 2.1 1 leader

Sources: Official leaderboards, boards copied by Epoch AI (CC BY 4.0) and labs’ model cards

More in Computer and browser use

All 69 in Computer and browser use
  • BrowseComp In use Hard-to-find facts that need persistent web browsing. Short verifiable answers graded for exact correctness. Claude Fable 5 93.3%
  • ScreenSpot-Pro In use GUI grounding on high-resolution professional-software screenshots. The model must locate the target UI element. GPT-6 Astra 92.7%
  • AndroidWorld In use Agents complete 116 tasks across 20 real Android apps in an emulator. Each task checks the device's system state to judge success. GPT-6 Astra 100%
  • WebArena-Verified In use Agents complete 812 audited WebArena tasks on self-hosted websites. Deterministic evaluators check the agent's answer and captured network traces. Muse Spark 1.1 69%
  • MobileWorld In use Agents complete 201 tasks across 20 mobile apps, including tasks that need user clarification or MCP tools. Backend checks verify success. GPT-5 55.3%
  • RecreationBench In use Agents explore a running reference app and rebuild it, across 250 tasks on five platforms. Programmatic tests and a VLM judge score the result. GPT-6 Astra 58.1%