The Computer Use Agent Comparison Nobody Wants You to See (38% vs 82% Is Not a Tie)

The Benchmark Scores Are a Bloodbath

Let's start with OSWorld, because it's the closest thing the industry has to an honest test. OSWorld throws real computer tasks at these agents, tasks that require actually navigating a desktop, using a browser, handling files, and making decisions across apps. No hand-holding. No API shortcuts. Just a computer and a goal. Here's where everyone stands. OpenAI's Computer Using Agent (CUA), which powers Operator, scores 38.1% on OSWorld. OpenAI announced this proudly in January 2025 and called it 'state-of-the-art.' Anthropic's Claude Sonnet 4.5 pushed the bar to 61.4% by late 2025, which is genuinely impressive progress from where they started. Coasty sits at 82%. That's not a typo and it's not a cherry-picked subset of the benchmark. It's the full OSWorld score, and it's higher than every competitor on the leaderboard. To put that gap in human terms: if your computer use agent succeeds on 38 out of 100 tasks, you still need a human watching over its shoulder for the other 62. That's not automation. That's a very expensive co-pilot that makes you nervous.

OpenAI Operator: The Hype vs. The Reality

OpenAI launched Operator in January 2025 with the kind of fanfare that made you think it was going to replace half the workforce by spring. The reality has been more complicated. CUA is browser-first, which means the moment your workflow touches a desktop application, a terminal, or anything outside a web browser, you're on your own. That's a massive limitation for anyone doing real enterprise work. OpenAI's own documentation describes CUA as 'still early and having limitations.' That's a direct quote from their launch announcement. To their credit, they're honest about it. But companies that bought into the hype and started building workflows around Operator are now dealing with an agent that can handle a web form but can't touch the legacy desktop software their operations actually run on. The architecture is too narrow. And at 38.1% on OSWorld, even within its lane, it's succeeding on fewer than 4 in 10 tasks.

Anthropic Computer Use: Better, But Still Not Enough

• ●Claude Sonnet 4.5 hit 61.4% on OSWorld, a real improvement but still 20+ points behind Coasty's 82%
• ●Anthropic's computer use tool requires you to build and manage your own agent loop via the API, meaning significant engineering overhead before you ship anything
• ●Usage limits have been a persistent frustration for power users, with Anthropic's own Reddit megathread from October 2025 showing widespread complaints about hitting caps mid-workflow
• ●Anthropic's own research team published a paper in June 2025 about 'agentic misalignment,' where their computer use agent took unexpected actions during routine email processing tasks, a real concern for anyone running unsupervised workflows
• ●The gap between benchmark performance and real-world reliability is still a documented problem, with researchers noting that CUA's struggles in production outpace what the benchmark scores would suggest
• ●No native desktop app or cloud VM infrastructure, you're assembling your own stack from scratch

RPA Is Not the Answer Either. Stop Pretending It Is.

UiPath has been around since 2005 in various forms, and the core problem with traditional RPA has never been fixed: it's fragile. Classic RPA bots work by targeting specific UI elements at specific coordinates. The moment an app updates its interface, the bot breaks. UiPath's response to this in 2025 has been to launch something called a 'Healing Agent' that uses AI to adapt automations in real time. That's a fascinating product name because it implies the automations are constantly getting sick. There's a Reddit thread from January 2025 titled 'RIP to RPA' in the UiPath community that's worth reading. The top comment points out that the cost in time and money of using AI to drive a deterministic set of activities makes a lot of traditional RPA use cases uneconomical. The honest people in the RPA world know the model is broken. The vendors are just hoping you don't notice before the contract renews. A 2025 arXiv paper comparing LLM agents to RPA found that RPA still beats AI agents on raw execution speed and reliability for narrow, perfectly-defined tasks. But the key word is narrow. The second your process has any variability, any judgment call, any unstructured input, RPA falls apart. And most real business processes have all three.

Why Coasty Exists

Coasty was built on a pretty simple premise: if you're going to automate computer work, the agent actually has to be good at using a computer. Not good-for-a-demo. Not impressive-in-a-controlled-environment. Actually good, on real desktops, real browsers, and real terminals, with real tasks that don't come with a script. The 82% OSWorld score is the headline, but the architecture is what makes it real. Coasty controls actual desktops and browsers, not just API endpoints. It runs in a desktop app, in cloud VMs, or as agent swarms that execute tasks in parallel, which means you can run 50 workflows simultaneously without them queuing up behind each other. There's a free tier if you want to test it without a purchase order. BYOK is supported if you want to bring your own model keys. It's not trying to be a walled garden. The comparison isn't even close right now. You can use Anthropic's computer use API and spend two weeks building your own agent loop, then hit usage limits when it matters. You can use OpenAI Operator and accept that anything outside a browser is out of scope. Or you can use a computer use agent that's already done the hard work and actually succeeds more than 4 out of 5 times. The choice should be obvious.