The Computer Use Agent Comparison Nobody Wants to Publish (Because It's Brutal)

The Benchmark Numbers Are a Bloodbath

Let's start with OSWorld, the industry-standard benchmark for computer use agents. It tests real tasks on real operating systems, not toy demos. Here's what the leaderboard actually looks like. Anthropic's Computer Use, the tool they spent months hyping with cinematic demo videos, scores 22% on OSWorld. OpenAI's Computer-Using Agent (CUA), which powers Operator and the ChatGPT agent, does better at 38.1%. That's still failing nearly two-thirds of tasks. Coasty sits at 82%. That's not a rounding error. That's a different category of product. When WorkOS published their head-to-head comparison of Anthropic versus OpenAI's computer use tools, they noted OpenAI's CUA also beat Anthropic on WebVoyager browser tasks. So Anthropic is losing to OpenAI AND losing badly to Coasty. Meanwhile Reddit threads about computer-using AI are full of people saying the same three words: 'too expensive, too slow, too unreliable.' They're describing the bottom of the market and calling it the whole market. That's the mistake.

What 'Too Slow and Too Unreliable' Actually Costs You

• ●Manual data entry alone costs U.S. companies $28,500 per employee per year, according to a 2025 Parseur survey of 1,000+ businesses.
• ●Employees lose an estimated 50 days per year to repetitive tasks, per WorkTime's 2026 productivity research.
• ●56% of employees report burnout directly tied to repetitive manual work, not workload, not management, specifically the copy-paste grind.
• ●Gartner data shows at least 69% of workers spend two or more hours daily on tasks that could be automated right now.
• ●A single Microsoft customer case study cited eliminating 6 to 8 hours per day of manual reconciliation work after deploying AI agents.
• ●If your team has 10 people doing any meaningful amount of manual computer work, you're likely burning $285,000 a year before you even account for errors and rework.

Why Anthropic and OpenAI Keep Whiffing on Computer Use

Here's the thing about Anthropic and OpenAI: computer use is a side feature for them. Claude is a chat model that also does computer use. GPT-4o is a reasoning model that also does computer use. Their core incentive is to make the best language model, and computer use is bolted on. That's why Claude's Computer Use tool requires you to build your own infrastructure, manage your own virtual machines, and handle your own orchestration. It's an API capability, not a product. OpenAI's Operator is more polished, but it's browser-only and it got quietly folded into the general ChatGPT agent in July 2025, which tells you how much priority it gets on their roadmap. UiPath and the legacy RPA crowd have the opposite problem: they built for a world where automation meant brittle scripts that broke every time a UI changed. Their 'intelligent automation' rebrand hasn't fixed the fundamental issue that RPA requires IT teams, maintenance cycles, and change management overhead that eats the ROI before you ever see it. Neither approach is built from the ground up to actually control a computer the way a human does.

The 'Dead End' Critics Are Solving the Wrong Problem

The June 2025 'computer use agents seem like a dead end' argument got real traction, and I understand why. If you've watched Anthropic's demo and then tried to use the actual product, the gap is demoralizing. The critics are right that most current computer use agents are too slow for interactive workflows, too fragile for production environments, and too expensive to justify for simple tasks. They're wrong to conclude that the category is broken. They're looking at 22% OSWorld scores and assuming that's the ceiling. It's not. The ceiling is 82% and climbing. The actual argument should be: most computer use AI products are bad, and you should stop using the bad ones. That's a very different conclusion than 'don't automate.' The companies quietly deploying high-accuracy computer use agents right now are going to have a massive operational advantage over the ones who read a frustrated blog post and decided to wait.

Why Coasty Exists and Why the Score Gap Matters in Practice

Coasty was built as a computer use agent first, not a chat model with automation features stapled to it. The 82% OSWorld score isn't just a number to put in a press release. It means that when you deploy Coasty on a real workflow, it actually finishes the task. It controls real desktops, real browsers, and real terminals. Not API wrappers, not browser extensions, actual computer control the way a human operator would do it. You get a desktop app for local work, cloud VMs for scalable deployment, and agent swarms that run tasks in parallel so you're not waiting on a single agent to grind through a queue. There's a free tier to test it without a procurement process, and BYOK support if you want to bring your own model keys. The reason this matters beyond the benchmark: when an agent fails 60 to 78 percent of tasks like Anthropic and OpenAI's tools do, you can't put it in a real workflow. You're babysitting it. When failure rates drop to the 18% range, you can actually walk away and let it run. That's the difference between a demo and a business tool.