OpenComputer
Computer-use agents complete 1,000 desktop tasks across 33 real applications, including browsers, office tools, creative software, development environments and messaging apps. App-specific verifiers inspect the final application state, and the harness gives partial credit from the recorded trajectory.
The tasks are generated by a pipeline and then finalized, and the verifiers improve themselves from execution feedback. The authors report that open models score much lower here than on OSWorld-Verified. 42 Hugging Face upvotes and about 32 GitHub stars.
- Made by
- Yale NLP Lab
- Released
- May 2026
- Kind
- Desktop apps
- Level
- Everyday
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- All public
- Strongest model tested
- GPT-5.4 #27 on the capability index
- Last model added
- April 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
We present OpenComputer, a verifier-grounded framework for constructing verifiable software worlds for computer-use agents. OpenComputer integrates four components: (1) app-specific state verifiers that expose structured inspection endpoints over real applications, (2) a self-evolving verification layer that improves verifier reliability using execution-grounded feedback, (3) a task-generation pipeline that synthesizes realistic and machine-checkable desktop tasks, and (4) an evaluation harness that records full trajectories and computes auditable partial-credit rewards. In its current form, OpenComputer covers 33 desktop applications and 1,000 finalized tasks spanning browsers, office tools, creative software, development environments, file managers, and communication applications. Experiments show that OpenComputer's hard-coded verifiers align more closely with human adjudication than LLM-as-judge evaluation, especially when success depends on fine-grained application state. Frontier agents struggle with end-to-end completion despite partial progress, and open-source models exhibit sharp drops from their OSWorld-Verified scores, exposing a persistent gap in robust computer automation.
GPT-5.4 leads with 68.3% among models released by April 2026
Best score per entry from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders.
The newest model with a score here came out in April 2026, and nobody has tested a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon
- GPT-6 Astra
- Claude Opus 5.5
- and 16 more
- 1 GPT-5.4 68.3%
- 2 Claude Sonnet 4.6 64.4%
- 3 Kimi K2.6 58.8%
- 4
Qwen3.5-27B 32.3%
- 5
Gemini 3 Flash 16.4%
- 6
EvoCUA-8B 10.9%
- 7
Qwen3.5-9B 7.80%
- 8 GUI-OWL-1.5-8B 5.70%
Source: Official OpenComputer leaderboard
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-5.4 | 68.3% | Mar 2026 | Official leaderboard | |
| 2 | Claude Sonnet 4.6 | 64.4% | Feb 2026 | Official leaderboard | |
| 3 | Kimi K2.6 | 58.8% | Apr 2026 | Official leaderboard | |
| 4 | | 32.3% | Feb 2026 | Official leaderboard | |
| 5 | | 16.4% | Dec 2025 | Official leaderboard | |
| 6 | | 10.9% | Jan 2026 | Official leaderboard | |
| 7 | | 7.80% | Feb 2026 | Official leaderboard | |
| 8 | GUI-OWL-1.5-8B | 5.70% | Official leaderboard |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Official OpenComputer leaderboard
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Official OpenComputer leaderboard
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
More 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%
- OSWorld 2.1 In use 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. Claude Opus 5.5 81.8%
- 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%