OSWorld-Science
Computer-use agents do 146 scientific software tasks, such as molecular drawing, across several domains. Produced artifacts are checked to score each task.
Separate from the OSWorld family by XLANG Lab. 40 upvotes on Hugging Face papers.
- Made by
- DiscoAILab
- Released
- September 2026
- Kind
- Desktop apps
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- All public
- Strongest model tested
- GPT-6 Astra #3 on the capability index
- Last model added
- September 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
Scientific software presents a demanding test for computer-using agents based on visual language models (VLMs): completing a research workflow requires interpreting specialized interfaces, manipulating scientific objects, and producing verifiable results. We thus introduce OSWorld-Science, a benchmark and evaluation environment that combines scientifically meaningful tasks, artifact-based evaluation, and an efficient agent harness for studying computer use in the scientific domain. The benchmark contains 12 VLMs and 146 high-quality tasks across several scientific domains and software configurations, covering workflows such as molecular drawing and retrosynthesis, pathology image analysis, statistical computing, and physical simulation. Tasks are developed through expert proposals and iterative human–AI co-design, with selection guided by scientific value and difficulty. Task-specific execution-based evaluators inspect application states and generated artifacts, including molecular structures, segmentation masks, plots, and numerical results, and award partial credit for incomplete outcomes. Our special harness integrates model adapters, interaction-loop control, and trajectory logging to support comparisons of models and interaction strategies. Our results show that current state-of-the-art VLMs with a strong harness still face challenges in addressing key questions in the scientific domains. We also analyze the benchmarking results across multi-linguistics, reasoning efforts, context length and other factors and derive several important conclusions and directions to assist future development. Overall, we provide an integrated framework connecting expert-defined scientific goals to verifiable software outcomes, enabling systematic evaluation of both agent capabilities and harness design in scientific workflows.
Claude Fable 5.1 leads with 73.7% among models released by September 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. Every run is in the next chart. 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 September 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
- Claude Opus 5.5
- GPT-6.1 Sol
- and 11 more
- 1 Claude Fable 5.1 v2026.09; default 73.7%
- 2 Claude Opus 5 v2026.09; default 57.9%
- 3 GPT-6 Astra v2026.09; default 57.4%
- 4 GPT-5.6 Sol v2026.09; default 50.1%
- 5 GPT-5.6 Terra v2026.09; default 31.8%
- 6 Kimi K3 v2026.09; default 30.2%
- 7 Claude Sonnet 5 v2026.09; default 26.3%
- 8 GPT-5.6 Luna v2026.09; default 22.6%
- 9
Gemini 3.1 Pro v2026.09; default 17.2%
- 10 Qwen-CUA v2026.09; default 15.8%
- 11
Qwen3.7-Plus v2026.09; default 15.2%
- 12
MiniMax-M3 v2026.09; default 13%
Source: Official OSWorld-Science leaderboard
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Fable 5.1 | 73.7% | v2026.09; default | Sep 2026 | Official leaderboard |
| 2 | Claude Opus 5 | 57.9% | v2026.09; default | Jul 2026 | Official leaderboard |
| 3 | GPT-6 Astra | 57.4% | v2026.09; default | Sep 2026 | Official leaderboard |
| 4 | GPT-5.6 Sol | 50.1% | v2026.09; default | Jul 2026 | Official leaderboard |
| 5 | GPT-5.6 Terra | 31.8% | v2026.09; default | Jul 2026 | Official leaderboard |
| 6 | Kimi K3 | 30.2% | v2026.09; default | Jul 2026 | Official leaderboard |
| 7 | Claude Sonnet 5 | 26.3% | v2026.09; default | Jun 2026 | Official leaderboard |
| 8 | GPT-5.6 Luna | 22.6% | v2026.09; default | Jul 2026 | Official leaderboard |
| 9 | | 17.2% | v2026.09; default | Feb 2026 | Official leaderboard |
| 10 | Qwen-CUA | 15.8% | v2026.09; default | Sep 2026 | Official leaderboard |
| 11 | | 15.2% | v2026.09; default | Jun 2026 | Official leaderboard |
| 12 | | 13% | v2026.09; default | Jun 2026 | Official leaderboard |
Partial score versus task cost
The line joins the runs that no other run beats on both measures. We redrew this chart from the numbers on the official leaderboard.
| Model | Setting | Partial score | Cost per task |
|---|---|---|---|
| Claude Fable 5.1 | v2026.09; default | 73.7% | $5.85 |
| Claude Opus 5 | v2026.09; default | 57.9% | $5.7 |
| GPT-6 Astra | v2026.09; default | 57.4% | $6.44 |
| GPT-5.6 Sol | v2026.09; default | 50.1% | $2.54 |
| GPT-5.6 Terra | v2026.09; default | 31.8% | $1.79 |
| Kimi K3 | v2026.09; default | 30.2% | $10.31 |
| Claude Sonnet 5 | v2026.09; default | 26.3% | $3.51 |
| GPT-5.6 Luna | v2026.09; default | 22.6% | $0.21 |
| | v2026.09; default | 17.2% | $3.12 |
| Qwen-CUA | v2026.09; default | 15.8% | $0.42 |
| | v2026.09; default | 15.2% | $0.41 |
| | v2026.09; default | 13% | $0.38 |
The best score, 73.7%, costs $5.85 a task
Runs on the cost frontier score higher than any run that's cheaper. The costs are the leaderboard's own estimates, so compare them only within this benchmark.
Source: Official OSWorld-Science leaderboard
| Model | Effort | Harness | Score | Cost per task | Output tokens |
|---|---|---|---|---|---|
| Claude Fable 5.1 | v2026.09; default | 73.7% | $5.85 | 21,020 | |
| Claude Opus 5 | v2026.09; default | 57.9% | $5.70 | 80,000 | |
| GPT-6 Astra | v2026.09; default | 57.4% | $6.44 | 12,298 | |
| GPT-5.6 Sol | v2026.09; default | 50.1% | $2.54 | 14,044 | |
| GPT-5.6 Terra | v2026.09; default | 31.8% | $1.79 | 19,006 | |
| Kimi K3 | v2026.09; default | 30.2% | $10 | 109,298 | |
| Claude Sonnet 5 | v2026.09; default | 26.3% | $3.51 | 63,844 | |
| GPT-5.6 Luna | v2026.09; default | 22.6% | $0.21 | 22,515 | |
| | v2026.09; default | 17.2% | $3.12 | 156,737 | |
| | v2026.09; default | 15.2% | $0.41 | 29,028 | |
| | v2026.09; default | 13% | $0.38 | 27,503 |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Official OSWorld-Science 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 OSWorld-Science 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%