OSWorld 2.0
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.
Releases are versioned and some cards report the offline subset or partial score, so cross-card numbers are not directly comparable.
- Released
- June 2026
- Kind
- Desktop apps
- Level
- University
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- All public
- Strongest model tested
- GPT-6 Astra #3 on the capability index
- Last in a launch post
- October 2026
- Last model added
- October 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.
Claude Opus 4.8 leads with 20.6% among models released by June 2026
Best score per model 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.
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.
The newest model with a score here came out in June 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 15 more
- 1 Claude Opus 4.8 max 20.6%
- 2 Claude Opus 4.7 max 18.2%
- 3 GPT-5.5 xhigh, 150 steps 13%
- 4 Claude Sonnet 4.6 medium 9.30%
- 5 Kimi K2.6 enabled, 300 steps 4.60%
- 6
MiniMax-M3 enabled 4.60%
- 7
Qwen3.7-Plus thinking 2.80%
Source: Official OSWorld 2.0 leaderboard
- 77.9 Claude Fable 5.1
- 72.6 GPT-6 Astra
- 71.4 GPT-6.1 Sol
- 70.6 Claude Opus 5
- 69.2
Gemini 4 Argon
- 66.9
Muse Spark 1.3
- 64.4 GPT-6 Sol
- 62.6 GPT-5.6 Sol
- 61.7
MiniMax-M3.1-Flash
- 59.1 GLM-5.3-Flash
- 59.0
Gemini 3.8 Flash
- 58.3 Kimi K3
- 52.7 GPT-6 Luna
- 50.2 GPT-5.6 Terra
- 47.9
Gemini 3.7 Flash
- 47.3
Muse Spark 1.1
- 45.6 GPT-5.6 Luna
- 20.6 Claude Opus 4.8
- 19.4
Qwen 3.8 Max
- 19.4
Qwen3.8 Flash
- 18.2 Claude Opus 4.7
- 13.0 GPT-5.5
- 9.30 Claude Sonnet 4.6
- 4.60 Kimi K2.6
- 4.60
MiniMax-M3
- 2.80
Qwen3.7-Plus
- 1 Claude Fable 5.1 77.9%
- 2 GPT-6 Astra 72.6%
- 3 GPT-6.1 Sol 71.4%
- 4 Claude Opus 5 70.6%
- 5
Gemini 4 Argon 69.2%
- 6
Muse Spark 1.3 66.9%
- 7 GPT-6 Sol 64.4%
- 8 GPT-5.6 Sol 62.6%
- 9
MiniMax-M3.1-Flash 61.7%
- 10 GLM-5.3-Flash 59.1%
- 11
Gemini 3.8 Flash 59%
- 12 Kimi K3 58.3%
- 13 GPT-6 Luna 52.7%
- 14 GPT-5.6 Terra 50.2%
- 15
Gemini 3.7 Flash 47.9%
- 16
Muse Spark 1.1 47.3%
- 17 GPT-5.6 Luna 45.6%
- 18 Claude Opus 4.8 max 20.6%
- 19
Qwen 3.8 Max 19.4%
- 20
Qwen3.8 Flash 19.4%
- 21 Claude Opus 4.7 max 18.2%
- 22 GPT-5.5 xhigh, 150 steps 13%
- 23 Claude Sonnet 4.6 medium 9.30%
- 24 Kimi K2.6 enabled, 300 steps 4.60%
- 25
MiniMax-M3 enabled 4.60%
- 26
Qwen3.7-Plus thinking 2.80%
Sources: Labs’ model cards and official OSWorld 2.0 leaderboard
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 4.8 | 20.6% | max, batched tool | May 2026 | Official leaderboard |
| 2 | Claude Opus 4.7 | 18.2% | max, batched tool | Apr 2026 | Official leaderboard |
| 3 | GPT-5.5 | 13% | xhigh, 150 steps, batch tool | Apr 2026 | Official leaderboard |
| 4 | Claude Sonnet 4.6 | 9.30% | medium, standard | Feb 2026 | Official leaderboard |
| 5 | Kimi K2.6 | 4.60% | enabled, 300 steps, standard | Apr 2026 | Official leaderboard |
| 6 | | 4.60% | enabled, standard | Jun 2026 | Official leaderboard |
| 7 | | 2.80% | thinking, standard | Jun 2026 | Official leaderboard |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Fable 5.1 | 77.9% | Sep 2026 | Lab model card | |
| 2 | GPT-6 Astra | 72.6% | Sep 2026 | Lab model card | |
| 3 | GPT-6.1 Sol | 71.4% | Sep 2026 | Lab model card | |
| 4 | Claude Opus 5 | 70.6% | Jul 2026 | Lab model card | |
| 5 | | 69.2% | Oct 2026 | Lab model card | |
| 6 | | 66.9% | Sep 2026 | Lab model card | |
| 7 | GPT-6 Sol | 64.4% | Sep 2026 | Lab model card | |
| 8 | GPT-5.6 Sol | 62.6% | Jul 2026 | Lab model card | |
| 9 | | 61.7% | Oct 2026 | Lab model card | |
| 10 | GLM-5.3-Flash | 59.1% | Aug 2026 | Lab model card | |
| 11 | | 59% | Sep 2026 | Lab model card | |
| 12 | Kimi K3 | 58.3% | Jul 2026 | Lab model card | |
| 13 | GPT-6 Luna | 52.7% | Sep 2026 | Lab model card | |
| 14 | GPT-5.6 Terra | 50.2% | Jul 2026 | Lab model card | |
| 15 | | 47.9% | Aug 2026 | Lab model card | |
| 16 | | 47.3% | Jul 2026 | Lab model card | |
| 17 | GPT-5.6 Luna | 45.6% | Jul 2026 | Lab model card | |
| 18 | Claude Opus 4.8 | 20.6% | max, batched tool | May 2026 | Official leaderboard |
| 19 | | 19.4% | Aug 2026 | Lab model card | |
| 20 | | 19.4% | Aug 2026 | Lab model card | |
| 21 | Claude Opus 4.7 | 18.2% | max, batched tool | Apr 2026 | Official leaderboard |
| 22 | GPT-5.5 | 13% | xhigh, 150 steps, batch tool | Apr 2026 | Official leaderboard |
| 23 | Claude Sonnet 4.6 | 9.30% | medium, standard | Feb 2026 | Official leaderboard |
| 24 | Kimi K2.6 | 4.60% | enabled, 300 steps, standard | Apr 2026 | Official leaderboard |
| 25 | | 4.60% | enabled, standard | Jun 2026 | Official leaderboard |
| 26 | | 2.80% | thinking, standard | Jun 2026 | Official leaderboard |
Labs report scores for 19 models in their cards. Switch the scores above to rank them together.
Of runs with a cost, the best scores 13.9% for $36 a task
Runs on the cost frontier score higher than any run that's cheaper. Claude Sonnet 4.6 at medium comes within 4.6 points of the best for $14 a task, about half the cost. The costs are the leaderboard's own estimates, so compare them only within this benchmark.
Source: Official OSWorld 2.0 leaderboard
| Model | Effort | Harness | Score | Cost per task | Output tokens |
|---|---|---|---|---|---|
| Claude Opus 4.8 | max | batched tool | 20.6% | ||
| Claude Opus 4.8 | max | standard | 18.5% | ||
| Claude Opus 4.7 | max | batched tool | 18.2% | ||
| Claude Opus 4.7 | max | standard | 13.9% | $36 | |
| Claude Opus 4.7 | max, 300 steps | standard | 13% | $23 | |
| GPT-5.5 | xhigh, 150 steps | batch tool | 13% | $17 | |
| GPT-5.5 | xhigh, 300 steps | batch tool | 13% | $25 | |
| GPT-5.5 | xhigh | batch tool | 13% | $25 | |
| Claude Sonnet 4.6 | medium | standard | 9.30% | $14 | |
| Claude Sonnet 4.6 | medium, 300 steps | standard | 8.30% | $9.17 | |
| Claude Sonnet 4.6 | max | standard | 8.30% | $22 | |
| Claude Sonnet 4.6 | max, 300 steps | standard | 6.50% | $16 | |
| Claude Sonnet 4.6 | medium, 150 steps | standard | 4.60% | $3.80 | |
| Claude Sonnet 4.6 | max, 150 steps | standard | 4.60% | $7.41 | |
| Claude Opus 4.7 | max, 150 steps | standard | 4.60% | $9.54 | |
| Kimi K2.6 | enabled, 300 steps | standard | 4.60% | $5.59 | |
| Kimi K2.6 | enabled | standard | 4.60% | $6.56 | |
| | enabled | standard | 4.60% | $2.40 | |
| | enabled, 300 steps | standard | 3.70% | $1.69 | |
| | thinking | standard | 2.80% | $3.81 | |
| Kimi K2.6 | enabled, 150 steps | standard | 1.90% | $3.11 | |
| | enabled, 150 steps | standard | 1.90% | $0.80 | |
| | thinking, 300 steps | standard | 1.90% | $3.73 |
Number one over time 4 models have held number one with claims included since its release in June 2026
Lab claims count from the day the card came out, independent runs from the model’s release. We draw one row each for the benchmark’s own leaderboard and what labs claim in their cards.
Sources: Official OSWorld 2.0 leaderboard and labs’ model cards
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official OSWorld 2.0 leaderboard and labs’ model cards
8 labs put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| Oct 19, 2026 | 61.7 | OSWorld 2.0 | |
| Oct 13, 2026 | 69.2 | OSWorld-2.0 (Offline subset, Partial score) | |
| Sep 29, 2026 | GPT-6.1 Sol | 71.4 | OSWorld 2.0, offline set, partial reward (GPT-6.1 Sol Max) |
| Sep 22, 2026 | GPT-6 Luna | 52.7 | OSWorld 2.0, offline set (GPT-6 Luna max) |
| Sep 22, 2026 | GPT-6 Sol | 64.4 | OSWorld 2.0, offline set, partial reward (GPT-6 Sol max) |
| Sep 3, 2026 | GPT-6 Astra | 72.6 | OSWorld 2.0 (v2026.08.08, offline set, partial score) |
| Sep 2, 2026 | 66.9 | OSWorld 2.0 (Agentic computer use) partial 66.9 / binary 32.0 | |
| Sep 2, 2026 | 59 | OSWorld-2.0 (Partial score, batch tool enabled) | |
| Sep 1, 2026 | Claude Fable 5.1 | 77.9 | Computer use OSWorld 2.0 (partial) |
| Aug 26, 2026 | 19.4 | Computer use OSWorld 2.0 Binary 19.4 Partial 52.3 | |
| Aug 26, 2026 | GLM-5.3-Flash | 59.1 | OSWorld 2.0 |
| Aug 13, 2026 | 47.9 | OSWorld-2.0 | |
| Aug 3, 2026 | 19.4 | OSWorld 2.0 (binary / partial) 19.4 / 46.7 | |
| Jul 24, 2026 | Claude Opus 5 | 70.6 | Computer use OSWorld 2.0 |
| Jul 16, 2026 | Kimi K3 | 58.3 | OSWorld 2.0 |
| Jul 9, 2026 | GPT-5.6 Luna | 45.6 | OSWorld 2.0 |
| Jul 9, 2026 | GPT-5.6 Terra | 50.2 | OSWorld 2.0 |
| Jul 9, 2026 | GPT-5.6 Sol | 62.6 | OSWorld 2.0 (Sol, max) |
6 system cards report it, from 4 labs
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.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Oct 13, 2026 | | 69.2 | OSWorld-2.0, offline subset, partial score (results table, p.5) | Capabilities |
| Sep 2, 2026 | | 59 | OSWorld-2.0, partial score, batch tool enabled (results table, p.5) | Capabilities |
| Aug 13, 2026 | | 47.9 | OSWorld-2.0 (results table, p.5) | Capabilities |
| Jul 27, 2026 | Kimi K3 technical report | 58.3 | OSWorld 2.0 (Table 2) | Capabilities |
| Jul 24, 2026 | Claude Opus 5 system card | 70.6 | OSWorld 2.0, first-attempt success rate averaged over five runs, 70.57% in 8.12.3 (Table 8.1.A) | Capabilities |
| Jul 9, 2026 | | 47.3 | OSWorld 2.0 partial score, w/o exec (Figure 44). Binary = 14.2 | 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.
Sources: Official leaderboards, boards copied by Epoch AI (CC BY 4.0) and labs’ model cards
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%
- 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%