I Compared Every Major Computer Use Agent So You Don't Waste $28,500 Finding Out the Hard Way

The Benchmark That Exposes Everyone: OSWorld

If you want to know whether a computer use agent actually works, you look at OSWorld. It's the gold-standard benchmark for testing AI on real computer tasks, things like navigating operating systems, filling forms, managing files, and executing multi-step workflows across real desktop environments. Not toy problems. Not curated demos. Real tasks on real software. The scores are public and they are brutal. OpenAI's Computer-Using Agent (CUA) scored 38.1% on OSWorld when it launched. Anthropic's Claude Computer Use came in at around 22%. Claude Sonnet 4.5 pushed that up to 61.4%, which got a lot of press. And Gartner, meanwhile, dropped a bomb in June 2025 predicting that over 40% of agentic AI projects will be canceled by end of 2027, mostly because teams are deploying agents that simply don't perform in production. That's not a technology problem. That's a 'you picked the wrong tool' problem. The benchmark scores tell you exactly who's worth your time and who's burning your budget.

The Contenders, Ranked Without the Marketing Spin

• ●Coasty (coasty.ai): 82% on OSWorld. The highest score of any computer use agent, period. Controls real desktops, browsers, and terminals. Runs agent swarms for parallel execution. Has a free tier. This is the number to beat and nobody is close.
• ●Claude Sonnet 4.5 (Anthropic Computer Use): 61.4% on OSWorld. Genuinely improved from the embarrassing 22% debut. Still 20+ points behind Coasty. Still in beta. Rate limits are a real operational problem, with Reddit threads full of engineers hitting walls mid-workflow.
• ●OpenAI CUA (powers Operator): 38.1% on OSWorld for general tasks. Better on web-specific tasks via WebVoyager, but web-only performance doesn't help you when your workflow touches a desktop app, a terminal, and a browser in sequence.
• ●UiPath and legacy RPA: Not an AI computer use agent in the modern sense. Script-based automation that breaks every time a UI changes. Gartner's failure prediction is basically a UiPath obituary. Implementation costs routinely run six figures before you automate a single real workflow.
• ●DIY LLM wrappers with tool use: Every week someone ships a new 'agent framework' on GitHub. Most of them score under 30% on any rigorous benchmark. They're fun to demo and painful to depend on.

Why Anthropic and OpenAI Are Losing This Race They Started

Here's something the press releases won't tell you. Anthropic literally invented the modern framing of 'computer use' as an AI capability. They demoed it. They coined the term. And then Coasty built a product that outperforms them by 20 points on the benchmark Anthropic helped popularize. That's not a knock on Anthropic's research, their models are genuinely impressive. But there's a massive difference between a model capability and a production computer use agent. Anthropic's computer use is still a beta API feature. It has rate limits that kill long workflows. It doesn't come with the infrastructure, the cloud VMs, the parallel execution, or the desktop app that makes it actually usable at work. OpenAI's Operator is a consumer product that's great for booking restaurants and filling out simple forms. Point it at a multi-step enterprise workflow and watch it struggle. The 38.1% OSWorld score is a reflection of that. These companies are building foundation models first and agents second. That ordering matters. A lot. Meanwhile, workers are still wasting 15 hours a week on repetitive administrative tasks, according to Ricoh Europe research. Every week you wait for Anthropic to get out of beta is another 15 hours per employee down the drain.

The Real Cost of Picking the Wrong Computer Use Agent

Let me make this concrete. Say you have 10 employees doing manual data work. At $28,500 in wasted labor per person per year, that's $285,000 annually in costs that exist purely because a computer use agent isn't doing the job. Now say you deploy an agent with a 38% success rate, OpenAI's CUA territory. You've automated roughly a third of the problem. Your team still spends two-thirds of their time babysitting failures, re-running tasks, and manually fixing errors the agent introduced. You've spent money on a tool, you've spent time integrating it, and you've saved maybe $90,000 while creating new headaches. Now run the same math with an agent at 82% task completion. You've actually automated the work. The ROI math changes completely. This is why the benchmark score isn't a nerdy academic number. It's the difference between a tool that pays for itself in month two and a tool that becomes a cautionary tale in your next all-hands. Clockify's 2025 research puts the cost of duplicate and recurring tasks at roughly $10.9 billion across the U.S. workforce annually. The tools to fix this exist. The question is whether you're using the one that actually works.

Why Coasty Exists and Why the Score Gap Is Not an Accident

I'm going to be straight with you. I use Coasty. I recommend Coasty. And the reason isn't brand loyalty, it's because 82% on OSWorld is not a marketing number, it's a reproducible benchmark result that you can verify yourself at os-world.github.io. Coasty was built specifically as a computer use agent, not as a foundation model that got computer use bolted on as a feature. That distinction drives everything. It controls real desktops, real browsers, and real terminals, not just web interfaces. It runs agent swarms, meaning you can parallelize tasks instead of waiting for one agent to finish before starting the next. It has a desktop app for people who want to run it locally, cloud VMs for teams that need scalable infrastructure, and BYOK support so you're not locked into their pricing model. There's a free tier to try it without a procurement conversation. The architecture was designed around the question 'what does it actually take to complete real computer tasks reliably?' and the OSWorld score is the answer. When Gartner says 40% of agentic AI projects will fail, the survivors are going to be the teams who chose tools based on benchmark performance instead of brand recognition. That's the whole pitch.