Computer and browser use
We track 56 benchmarks in computer and browser use, or 64 if you count each version. They run from everyday level to expert level.
Anthropic held the top computer and browser use score 30% of the contested time
That's about 14 of the 46 months since January 2023, averaged over the benchmarks below. The easiest benchmarks are at the top. Versions of one test share a row, with a dark tick at each new version. We stop the clock, and fade the rest of the row, when the best score passes 85%, or at the newest score if 45 days go by without another.
OpenAI Qwen ByteDance Seed Alibaba Group Google DeepMind Microsoft Amazon Moonshot AI Anthropic Z.ai
Everyday Most adults, without training
University Undergraduates and entry-level professionals
Expert Specialists with years of training, often over hours of work
Share of contested time at number one
Anthropic 30% OpenAI 27% 
ByteDance Seed 21% 
Alibaba Group 9% 
Qwen 8% 
Google DeepMind 3% Z.ai 1% Moonshot AI 1%
ByteDance Seed 21% Alibaba Group 9% Qwen 8% Google DeepMind 3% Z.ai 1% Moonshot AI 1% Closed out: 8 benchmarks passed 85%
Google DeepMind 2 OpenAI 2 ByteDance Seed 2 Anthropic 1 Qwen 1 - Jun 2026 OSWorld-Verified Claude Fable 5
- May 2026 AndroidWorld Gemini 3.5 Flash
- Feb 2026 BrowseComp Gemini 3.1 Pro
- Dec 2025 ScreenSpot-Pro GPT-5.2
- Sep 2025 Online-Mind2Web UI-TARS-2
- Apr 2025 ScreenSpot-v2 UI-TARS-1.5
- Jan 2025 ScreenSpot Qwen2.5-VL-72B
- Jan 2025 WebVoyager OpenAI Computer-Using Agent (CUA)
Scores come from each benchmark’s own leaderboard, from Epoch AI’s runs and from labs’ reports on their own models. We date Epoch runs by the model’s release and lab reports by when they came out. Labs pick their own settings, so a lab’s claim can beat an independent run of the same model. Turn off lab claims to see independent runs only.
OpenAI Qwen Alibaba Group Google DeepMind ByteDance Seed Moonshot AI Anthropic Z.ai
Everyday Most adults, without training
University Undergraduates and entry-level professionals
Expert Specialists with years of training, often over hours of work
Share of contested time at number one, independent runs only
Anthropic 32% OpenAI 20% Qwen 19% Alibaba Group 12% ByteDance Seed 12% Google DeepMind 3% Z.ai 1%
Qwen 19% Alibaba Group 12% ByteDance Seed 12% Google DeepMind 3% Z.ai 1% Only each benchmark’s own leaderboard and Epoch AI’s runs, dated by the model’s release. We leave out benchmarks that only labs report.
From everyday to expert level
Grouped by who could solve the tasks, from things most adults can do to open research problems. The bar under each name is its best score.
Everyday
Most adults, without training
- OSWorld-Verified
- AndroidWorld
- OSWorld
- MobileWorld
- ScreenSpot-v2
- WebVoyager
- ScreenSpot
- Online-Mind2Web
- WebArena
- WebArena-Verified
- MyPCBench
- OSWorld-G
- Windows Agent Arena
- ClawBench V2
- ClawBench
- WebTailBench
- AndroidControl
- DeepShop
- MobileGym
- VisualWebBench
- AndroidDaily
- iOSWorld
- Android in the Zoo (AitZ)
- CreativeWork
- GroundUI
- Mind2Web
- MobileMiniWoB++
- MMBench-GUI
- AssistantBench
- BU Bench V2
- BU Bench V2.1
- CLASHarness
- CRAB
- MiniWoB++
- OSGym
- UI-Vision
- Verifiable Browsing Task (Meta)
- VisualAgentBench
- WARC-Bench
- WebCanvas
- WebLINX
- WebShop
- WorldGUI
University
Undergraduates and entry-level professionals
Expert
Specialists with years of training, often over hours of work
By kind
Desktop apps
| Benchmark | Level | Scored | Best score | Status |
|---|---|---|---|---|
| OSWorld-Verified 369 open-ended tasks in real desktop apps. Agent acts through screenshots, mouse and keyboard. Execution-based checkers score success. | Everyday | Checked automatically | 86% | In use |
| 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. | University | Checked automatically | 20.6% | In use |
| OSWorld Agents complete 369 real computer tasks in Ubuntu desktop apps through screenshots and mouse and keyboard actions. Execution-based scripts check the final state. | Everyday | Checked automatically | 72.1% | Fading |
| RecreationBench Agents explore a running reference app and rebuild it, across 250 tasks on five platforms. Programmatic tests and a VLM judge score the result. | Expert | Checked automatically | 58.1% | In use |
| OSWorld 2.1 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. | University | Checked automatically | 81.8% | In use |
| MyPCBench Computer-use agents work in a personalized Linux desktop with 17 simulated logged-in web apps and a user's history, across 184 tasks. | Everyday | Checked automatically | 62% | Not in cards |
| Windows Agent Arena Agents complete more than 150 tasks in a real Windows OS with its apps, tools and browsers. Scripts adapted from OSWorld check the final state. | Everyday | Checked automatically | 50.6% | Dropped |
| OSWorld 2.1 (offline subset) Agents complete the 82 OSWorld 2.1 tasks on the official offline list, with no internet access. Scored by checkpoint partial credit and strict pass rate. | University | Checked automatically | 81.5% | In use |
| CUA-World-Long Computer-use agents do one long task in each of 200 real applications, such as 3D Slicer, GIMP and OpenEMR, often needing 500 or more steps. Checklist verifiers with privileged state score each task. | Expert | Checked automatically | 27.5% | Not in cards |
| OSWorld 2.0 (Aug 2026) 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. | University | Checked automatically | 77.9% | System cards only |
| CreativeWork Agents complete productivity tasks in Notion, Canva and Figma by mixing GUI actions with MCP tool calls. | Everyday | Checked automatically | 42.5% | In use |
| CLASHarness Computer-use agents work in multi-agent setups where peers report stale states, give contradictory instructions or compete for files. Agents must check claims against the environment. | Everyday | Checked automatically | Not in cards | |
| CRAB Multimodal agents do tasks that span an Ubuntu desktop and an Android phone at once. Graph-based evaluators give credit for intermediate steps. | Everyday | Checked automatically | Not in cards | |
| OSGym Infrastructure running 1,000+ parallel OS sandboxes for computer-use agent training and evaluation. Tasks span browsing, documents, SWE and multi-app work. | Everyday | Checked automatically | Not in cards | |
| 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. | Expert | Checked automatically | Not in cards | |
| ScienceBoard Computer-use agents do scientific workflows in desktop software such as ChimeraX, Celestia, GRASS GIS and Lean 4. The apps are modified to expose state for checking. | Expert | Checked automatically | Not in cards | |
| Spider2-V Multimodal agents do data science and engineering workflows in real enterprise apps such as BigQuery, dbt and Airbyte, on a desktop computer. Execution-based checks score each task. | Expert | Checked automatically | Not in cards | |
| WeaveBench Computer-use agents do 114 long tasks in 8 work domains that mix GUI actions with command-line and code operations in one trajectory. Results are checked against verifiable artifacts. | Expert | Checked automatically | Not in cards | |
| WorldGUI Desktop GUI agents do 611 tasks that start from varied initial states, such as the target app not being open. Tasks come with instructional videos and project files. | Everyday | Checked automatically | Not in cards |
Phones
| Benchmark | Level | Scored | Best score | Status |
|---|---|---|---|---|
| AndroidWorld Agents complete 116 tasks across 20 real Android apps in an emulator. Each task checks the device's system state to judge success. | Everyday | Checked automatically | 100% | In use |
| MobileWorld Agents complete 201 tasks across 20 mobile apps, including tasks that need user clarification or MCP tools. Backend checks verify success. | Everyday | Checked automatically | 74.4% | In use |
| AndroidControl Agents predict the next action on Android app screens from either a high-level goal or a low-level step instruction. Scored by step accuracy against human demonstrations. | Everyday | Checked automatically |  91.2% | Dropped |
| MobileGym Mobile GUI agents use 28 simulated apps in a browser-hosted phone, across 416 parameterized task templates. Deterministic judges check the app state. | Everyday | Checked automatically | 58.8% | Not in cards |
| AndroidDaily Agents complete 350 daily tasks in 94 real closed-source Android apps. A process-aware reviewer model checks each trajectory against written guidelines. | Everyday | Graded by a model |  61.9% | Fading |
| iOSWorld Phone agents work in a native iOS simulator across 26 purpose-built apps that share one persistent user's data. Tasks span single apps, multi-app flows and memory or personalization from the user's history. | Everyday | Checked automatically | 51.9% | Not in cards |
| Android in the Zoo (AitZ) Models predict the next action on Android app screens, given the goal and past steps. Scored by exact match of the predicted action on the test split. | Everyday | Checked automatically | 83.2% | Dropped |
| AndroidLab Mobile agents complete 138 tasks across nine apps on predefined Android virtual devices, in text-only and multimodal modes. Checks on the final device state score each task. | University | Checked automatically | 31.2% | Not in cards |
| MobileMiniWoB++ Agents complete MiniWoB++ web tasks inside the Android emulator of AndroidWorld. Scored by task success rate. | Everyday | Checked automatically | 68% | Dropped |
Web browsing
| Benchmark | Level | Scored | Best score | Status |
|---|---|---|---|---|
| BrowseComp Hard-to-find facts that need persistent web browsing. Short verifiable answers graded for exact correctness. | Expert | Checked automatically | 93.3% | In use |
| WebVoyager Agents complete user tasks on 15 popular real websites by browsing from screenshots and page text. A GPT-4V judge grades whether each task succeeded. | Everyday | Graded by a model |  89.3% | Fading |
| ApprenticeBench Agent joins a simulated company and processes 100 accounts-payable bills in Odoo via GUI or API, learning from records and feedback. Cumulative success rate. | University | Checked automatically | 72% | Not in cards |
| Online-Mind2Web Web agents do 300 tasks on 136 live websites. An LLM judge reads the agent's trajectory and decides whether the task succeeded. | Everyday | Graded by a model | 88.2% | Fading |
| WebArena Agents complete 812 realistic tasks on self-hosted shopping, forum, GitLab, CMS and map websites. Functional graders check the outcome. | Everyday | Checked automatically | 68% | Fading |
| WebArena-Verified Agents complete 812 audited WebArena tasks on self-hosted websites. Deterministic evaluators check the agent's answer and captured network traces. | Everyday | Checked automatically |  69% | In use |
| ClawBench V2 Browser agents do 130 new everyday tasks on live websites under six supported harnesses. Request interception and a more lenient LLM judge score each run. | Everyday | Graded by a model | 44.6% | Not in cards |
| ClawBench Browser agents do 153 everyday tasks on live websites, such as booking travel, ordering food and applying for jobs. Request interception stops real side effects, and an LLM judge scores the five-layer traces. | Everyday | Graded by a model | 61.4% | Not in cards |
| WebTailBench Computer-use agents complete 609 hand-verified tasks on live websites across 11 categories, such as bookings, real estate and job search. A companion set of 111 harmful tasks tests refusals. | Everyday | Graded by a model |  49.5% | Fading |
| SaaS-Bench 106 long-horizon GUI workflows across 23 locally deployed real SaaS apps. Scored from app databases with weighted checkpoints and full-resolution rate. | University | Checked automatically | 60.1% | In use |
| DeepShop Web agents answer complex shopping queries on live sites with product attributes, search filters and sorting preferences. An LLM judge checks whether results meet each requirement. | Everyday | Graded by a model | 42.3% | Fading |
| Mind2Web Models predict the next web action for over 2,000 crowdsourced tasks on 137 real websites in 31 domains. Scored by element accuracy and step success rate. | Everyday | Checked automatically | 55.8% | Dropped |
| AssistantBench Web agents do 214 realistic, time-consuming tasks on the open web, such as monitoring real-estate markets. Answers are checked automatically against gold answers. | Everyday | Checked automatically | Not in cards | |
| BrowseComp Extended Agents browse the web to find hard-to-locate facts with short answers. Mercor adds 100 new expert-written tasks to the 130 public ones and reports both scores. | Expert | Checked automatically | Not in cards | |
| BU Bench V2 Browser agents do real web tasks with the Browser Use library. A judge scores each run against a rubric. | Everyday | Graded by a model | Not in cards | |
| BU Bench V2.1 Browser agents do 200 real web tasks. A findings judge checks tool evidence, answers and files, and deterministic integrity checks run as well. | Everyday | Graded by a model | Not in cards | |
| CocoaBench Unified digital agents do long human-designed tasks that mix vision, search and coding. An automatic function checks each final output. | Expert | Checked automatically | Not in cards | |
| MiniWoB++ Agents operate over 100 small synthetic web pages, such as forms, menus and date pickers, from a natural-language instruction. Each task returns a success reward. | Everyday | Checked automatically | Not in cards | |
| Verifiable Browsing Task (Meta) Agents answer list-style information-seeking questions, written as realistic user requests, using browser search, open and find tools. | Everyday | Checked automatically | Not in cards | |
| VisualAgentBench Large multimodal models act as agents in embodied, GUI and visual design environments, including mobile, WebArena-based web and Minecraft tasks. | Everyday | Checked automatically | Not in cards | |
| VisualWebArena Multimodal web agents complete 910 visually grounded tasks on self-hosted classifieds, shopping and Reddit-style sites. Execution-based checks score each task. | University | Checked automatically | Not in cards | |
| WARC-Bench Screenshot-only agents do short web subtasks, such as date pickers and scroll containers, on real sites replayed from Web Archive snapshots. A program checks the end state. | Everyday | Checked automatically | Not in cards | |
| WebCanvas Web agents do tasks on live websites, scored on key intermediate states rather than one fixed action path, so the test survives site changes. | Everyday | Checked automatically | Not in cards | |
| WebLINX Agents follow a user's multi-turn chat instructions to navigate real websites. 100,000 interactions from 2,300 expert demonstrations give turn-level metrics. | Everyday | Checked automatically | Not in cards | |
| WebShop Agents search and click through a simulated shopping site with 1.18 million real products to buy an item that matches a text instruction. Reward reflects how well the purchase matches. | Everyday | Checked automatically | Not in cards | |
| WorkArena Web agents do routine knowledge-work tasks in a live ServiceNow instance, such as filling forms and filtering lists. WorkArena++ adds 682 compositional tasks at levels 2 and 3. | University | Checked automatically | Not in cards | |
| WorkArena++ Web agents complete 682 compositional knowledge-work tasks in ServiceNow, which need planning, reasoning and memory across many steps. | Expert | Checked automatically | Not in cards |
Finding things on screen
| Benchmark | Level | Scored | Best score | Status |
|---|---|---|---|---|
| ScreenSpot-Pro GUI grounding on high-resolution professional-software screenshots. The model must locate the target UI element. | University | Checked automatically | 92.7% | In use |
| ScreenSpot-v2 Models locate a described element on mobile, desktop and web screenshots. A click inside the target box counts as correct. | Everyday | Checked automatically | 95.2% | Fading |
| ScreenSpot Model locates a described UI element in mobile, desktop and web screenshots. Scored by click-point accuracy. | Everyday | Checked automatically | 95.4% | Fading |
| OSWorld-G Models map 564 desktop instructions to the right screen element or point, covering text matching, element recognition, layout understanding and fine manipulation. | Everyday | Checked automatically | 71.3% | Fading |
| VisualWebBench Models answer questions about web page screenshots across seven tasks, such as captioning, OCR, element grounding and action prediction. | Everyday | Checked automatically | 87.3% | Dropped |
| GroundUI Agents get a screenshot and an instruction and must click the right UI element. About 18K test samples cover web, desktop and mobile screens. Scored by whether the point falls in the target box. | Everyday | Checked automatically |  80.5% | Dropped |
| MMBench-GUI GUI agents are tested at four levels on Windows, macOS, Linux, iOS, Android and the web: content understanding, element grounding, task automation and task collaboration. | Everyday | Checked automatically | 81.5% | System cards only |
| ADeptS-Bench Computer-use agents pick the next interface action from a screenshot and instruction. A deterministic grader scores whether the action is safe. | Expert | Checked automatically | System cards only | |
| UI-Vision Computer use agents are tested offline on human desktop demonstrations from 83 applications. Three tasks go from element grounding to layout grounding to next-action prediction for clicks, drags and key presses. | Everyday | Checked automatically | Not in cards |