The Brutal Computer Use Agent Comparison Nobody Else Will Publish (2025)

The Benchmark Problem: Everyone's Cheating a Little

OSWorld is the gold standard for measuring computer use agents. It throws 369 real computer tasks at an AI, things like editing spreadsheets, navigating browsers, managing files, and running terminal commands, and scores how many it actually completes. It's the closest thing we have to a fair fight. But here's the thing: a researcher named Benjamin Anderson published a piece in August 2025 called 'Computer-Use Evals are a Mess,' and the title says everything. Benchmark conditions vary wildly. Some teams test on clean VMs with perfect setups. Others test on the exact environments their model was trained on. A few results on the OSWorld leaderboard come from 'specialized' models, meaning models trained specifically to ace this one benchmark rather than handle your actual messy work environment. When you strip away the favorable conditions and test these agents on real-world tasks, the scores collapse fast. Anthropic's Claude Sonnet 4.6 just got a blog post calling it 'a major improvement in computer use skills.' Microsoft published Fara-7B in November 2025 with impressive web benchmark numbers. OpenAI's Operator got rebranded into 'ChatGPT agent' in July 2025 after a review called it 'unfinished, unsuccessful, and unsafe.' That last one is a direct quote from a hands-on review. Not a competitor's marketing copy. A real person who used it.

Let's Go Competitor by Competitor

• ●Anthropic Computer Use: Claude's computer use tool is genuinely impressive at reasoning, but Anthropic's own research in June 2025 flagged 'agentic misalignment,' where Claude takes sophisticated unintended actions during computer use demos. That's not a reassuring sentence to read before you hand an AI your desktop.
• ●OpenAI Operator / ChatGPT Agent: Launched January 2025, rebranded by July 2025. A Partnership on AI report found Operator was 'taking screenshots instead of copying text, leading to OCR mistakes.' It also has a documented habit of declining tasks it considers sensitive, which in practice means it stops constantly. Researchers called it unfinished. OpenAI's own launch page lists 'task limitations' as a known feature.
• ●UiPath RPA: The old guard. UiPath just launched a 'Healing Agent' in July 2025 specifically to fix the fact that their automations break constantly when UI elements change. Their own blog post admits this is 'one of the most significant challenges facing RPA.' You've been paying enterprise licensing fees for a tool that needs a separate AI agent just to stop falling over.
• ●Microsoft Fara-7B: Small, efficient, designed to run on-device. Solid numbers on web benchmarks. But it's a research model, not a product you can deploy today. Microsoft's own framing is 'an efficient agentic model,' not a finished computer use solution.
• ●Coasty: 82% on OSWorld. That's the number. No other production-ready computer use agent is close to that score right now. It controls real desktops, real browsers, and real terminals, not just API wrappers pretending to do computer work.

Why RPA Is a Sunk Cost You Keep Defending

UiPath's 'Healing Agent' announcement is one of the most accidentally honest things published in tech this year. Read between the lines: their core product breaks so often when a button moves or a UI updates that they had to build a whole secondary AI system to patch the failures in real time. That's not innovation. That's duct tape on a product that was always fundamentally fragile. Traditional RPA works by recording exact pixel positions and element selectors. Change the font size on a webpage. Rename a dropdown. Update your CRM's interface. Suddenly your entire automation is down and someone's filing a support ticket. Human error rates in manual data entry run between 1% and 5%, according to V7 Labs. That sounds small until you're processing thousands of records and a 2% error rate is costing you real money in corrections, audits, and customer complaints. RPA promised to fix this. What it actually delivered was a different kind of fragile, one that requires constant maintenance from expensive RPA developers and breaks on a Tuesday because someone updated Chrome. A real computer use agent doesn't work from brittle selectors. It sees the screen the way a human does and figures out what to do. That's the actual difference.

The Hidden Cost Nobody Puts in the Pitch Deck

Smartsheet's research found that more than 40% of workers spend at least a quarter of their work week on manual repetitive tasks. The top three things they want automated are email, data collection, and data entry. These aren't exotic workflows. They're the basic plumbing of every business on earth. Now do the math on your team. If you have 20 people each losing 10 hours a week to tasks a computer use agent could handle, that's 200 hours a week. At even a modest $40 average hourly cost, you're burning $8,000 every single week. That's $416,000 a year, for one team of 20. Not on strategy. Not on growth. On copying data between screens. The reason companies haven't fixed this isn't that they don't know it's a problem. It's that the tools they tried, RPA, basic automation scripts, early AI tools, didn't actually work reliably enough to trust. So they gave up and hired more people instead. That calculation is about to look very different.

Why Coasty Exists

I'm not going to pretend I stumbled onto Coasty by accident. I was looking for a computer use agent that could actually handle the kind of messy, multi-step work that real jobs involve, not a demo that works once under perfect conditions. The 82% OSWorld score matters because OSWorld doesn't let you cherry-pick. It's 369 tasks across spreadsheets, browsers, file systems, and terminals. Coasty handles all of them with the same agent. It runs on a desktop app, on cloud VMs, and it supports agent swarms for parallel execution, meaning you can run multiple computer-using AI instances at once on different tasks. There's a free tier if you want to actually test it before committing, and BYOK support if you're the kind of person who wants control over your own model costs. The thing that actually sold me is that it controls real desktops and real browsers. Not a sandboxed simulation. Not API calls dressed up to look like computer use. The actual screen, the actual cursor, the actual applications your team uses every day. That's what separates a real computer use agent from a chatbot that learned to click buttons in a demo.