I Compared Every Major Computer Use Agent in 2025. Most of Them Are a Waste of Money.

The Dirty Secret Nobody Puts in Their Launch Blog Post

Employees currently spend up to 40% of their working hours on repetitive tasks that could be automated. At an average U.S. knowledge worker salary, that's roughly $25,000 to $35,000 per person per year being set on fire doing copy-paste work, tab-switching, and manual data entry. Multiply that across a team of 20 and you're looking at half a million dollars annually in pure productivity waste. That's the problem computer use agents are supposed to solve. The pitch is real. The urgency is real. But here's the part that should make you angry: most of the tools being sold as solutions to that problem can't reliably complete a multi-step task on a real desktop without breaking. The benchmark scores don't lie, even when the marketing does. OSWorld is the gold standard for measuring how well an AI agent can actually operate a computer. Not in a sandbox. Not with API shortcuts. On a real screen, with real software, doing real tasks. When you look at those scores side by side, the gap between the best and the rest isn't a rounding error. It's a chasm.

The Scorecard: What the Benchmarks Actually Show

• ●Coasty scores 82% on OSWorld, the highest of any computer use agent publicly benchmarked. Nobody else is close.
• ●Claude Sonnet 4.5 scores 61.4% on OSWorld. Anthropic called it 'a significant leap forward.' A 61% pass rate on real-world computer tasks is not a leap. It's a stumble in the right direction.
• ●OpenAI Operator launched in January 2025 and was described by one detailed independent reviewer as 'unfinished, unsuccessful, and unsafe.' That review was published in July 2025, six months after launch.
• ●The same reviewer noted that Anthropic's Computer Use shipped a full 12 months before Operator, and Operator still couldn't outperform it on basic tasks.
• ●Microsoft's Fara-7B is optimized for on-device use and scores competitively on narrow web benchmarks, but falls apart on general desktop tasks outside its training distribution.
• ●RPA tools like UiPath require brittle script-based automation that breaks every time a UI changes. One vendor's own partner network admits that 4 in 5 AI projects fail to deliver expected outcomes.
• ●The gap between 61% and 82% on OSWorld isn't academic. In production, that difference means your agent completes the task or it doesn't. There's no partial credit when you're running a real workflow.

Why OpenAI Operator Is a Case Study in Overpromising

OpenAI launched Operator with the kind of fanfare that makes you think the product is already finished. Sam Altman on stage, live demos, the whole show. Six months later, independent testing found it 'still doesn't work' on tasks that Claude Computer Use, for all its limitations, could handle. One writer who tested both put it plainly: Operator is late to the party and it still doesn't work. That's not a fringe opinion. That's what happens when you benchmark against the hype instead of the task. To be fair, OpenAI's CUA model is genuinely interesting from a research perspective. It combines GPT-4o vision with reinforcement learning, and the architecture is smart. But smart architecture and a working product are two different things. When you're trying to automate a workflow that touches five different applications, 'interesting architecture' doesn't get the job done. Reliability does. Speed does. And a computer use agent that requires constant hand-holding isn't automation. It's assisted clicking.

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

Every time I talk to an enterprise team that's been burned by a computer use agent, half of them are coming off a failed RPA implementation. UiPath and its competitors built an entire industry on the premise that you could script your way to automation. And for narrow, stable, predictable workflows, fine. But the moment a website updates its layout, or an application changes its button placement, or a new field appears in a form, the bot breaks. Someone has to fix it. That someone costs money. The maintenance burden of traditional RPA is one of the most underreported costs in enterprise software. You pay for the license, you pay for the implementation, and then you pay a team to babysit scripts that fall over every quarter. AI-powered computer use agents solve this because they see the screen the way a human does. They don't rely on element IDs or pixel coordinates. They reason about what they're looking at and figure out what to do. That's the actual breakthrough. Not the marketing. The architecture.

Why Coasty Exists and Why the 82% Score Matters More Than You Think

I'm going to be straight with you. I work at Coasty. But I also wouldn't work here if I didn't think the product was genuinely better, and the OSWorld score is the clearest evidence I can point to. 82% on the benchmark that every serious AI lab uses to measure real-world computer task performance. That's not a cherry-picked demo. That's a standardized test with hundreds of tasks across real desktop environments, real browsers, and real terminals. Coasty controls actual desktops, not simulated ones. It runs in a desktop app, in cloud VMs, and it supports agent swarms for parallel execution, so you're not waiting for one agent to finish before the next task starts. If you have a BYOK setup or want to start without committing budget, there's a free tier. The reason that 82% number matters isn't bragging rights. It's because every percentage point below that represents tasks your agent fails to complete, workflows that stall, and humans who have to step in and do the thing you bought the agent to do. At 61%, you're failing on nearly 4 out of 10 tasks. At 82%, you're completing the overwhelming majority of them. In production automation, that's not a marginal improvement. That's the difference between a tool that runs your workflows and a tool that runs your patience.