I Tested Every Major Computer Use Agent So You Don't Waste Six Months Finding Out They Suck

Why This Comparison Matters More Than Any Other AI Benchmark Right Now

Computer use agents are a fundamentally different category from chatbots. A chatbot answers questions. A computer use agent actually sits down at a computer, opens apps, clicks buttons, fills forms, reads screens, and gets work done. The difference sounds obvious but it's enormous in practice. The OSWorld benchmark is the closest thing we have to a standardized test for this. It throws real computer tasks at agents, things like editing spreadsheets, navigating web apps, managing files, and scores them on success rate. The numbers are humbling. OpenAI's CUA scored 38.1% on OSWorld when it launched. Anthropic's Claude Sonnet 4.5 hit 61.4%. Claude Opus 4.6 pushed to 72.7%. These are the industry leaders, and even the best of them fail on more than a quarter of tasks. That's the baseline you're working with. Now imagine deploying that in a production environment without knowing any of this. That's what most companies are doing right now.

The Competitors, Graded Without Mercy

• ●OpenAI Operator (ChatGPT Agent): Scored 38.1% on OSWorld at launch. Real users on Reddit reported hitting daily usage limits within 12 hours while the agent was still failing tasks. That's a double insult. You pay for Pro, the agent bricks your workflow, and then you're locked out for the day.
• ●Anthropic Claude Computer Use: Technically impressive and improving fast, from 42.2% to 72.7% across model versions. But it's a capability baked into Claude, not a purpose-built agent platform. You're stitching together your own infrastructure, your own VM setup, your own orchestration. Great research tool, incomplete product.
• ●Google Project Mariner: Lives inside a Chrome extension. That's the whole computer. A browser tab. If your workflows touch anything outside a browser, Mariner simply doesn't exist for you.
• ●UiPath Agentic Stack: UiPath themselves published data showing 30 to 50 percent of RPA projects initially fail. They've been pivoting toward AI agents for years but the core product is still brittle, selector-based automation that breaks every time a UI updates. Their own UI-CUBE benchmark is a transparent attempt to define evaluation criteria in a way that flatters their product.
• ●Coasty: 82% on OSWorld. 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 isn't a close race.

RPA Is Not the Answer and It Never Was

Let me be direct about something the enterprise software industry has been dancing around for a decade. RPA was always a band-aid. You hire consultants to map your processes, build fragile bots that depend on pixel-perfect UI selectors, and then spend half your automation budget maintaining those bots every time a vendor updates their interface. UiPath's own blog admitted that 30 to 50 percent of RPA projects initially fail. That's not a fringe statistic. That's from the company selling you the product. The promise of a computer use agent is completely different. Instead of scripting every click in advance, a true AI computer use agent looks at the screen the way a human does, figures out what needs to happen, and does it. No selectors. No brittle scripts. No maintenance hell every time Salesforce pushes an update. The human error rate on manual data entry sits between 1 and 5 percent. A well-built computer use agent can get that to near zero while running 24 hours a day. The math on this is not complicated. The adoption curve is just slow because most of the available tools aren't good enough yet.

What Actually Separates a Good Computer Use Agent From a Toy

After testing these tools extensively, here's what I think actually matters. First, benchmark scores are real signal. OSWorld is hard to game because it uses actual computer environments with actual tasks. A 38% score means the agent fails on 62% of real tasks. That's not a tool you can build a business process around. Second, infrastructure matters as much as the model. A computer use agent that only works in a browser (Mariner) or requires you to build your own VM stack (raw Claude API) isn't a product, it's a proof of concept. Third, parallelization is the multiplier everyone ignores. If your agent can only run one task at a time, you've automated one human. If it can run agent swarms in parallel, you've automated a team. That distinction separates tools that save hours from tools that transform operations. Fourth, the free tier question is more important than it sounds. If you can't test a computer-using AI on your actual workflows before committing budget, you're flying blind. The vendors who hide everything behind enterprise contracts are usually hiding mediocre performance too.

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

Coasty sits at 82% on OSWorld. The next closest competitor with a verified score is Claude Opus 4.6 at 72.7%. That's a 10-point gap at the top of the leaderboard, and in benchmark terms that's a canyon. Coasty wasn't built to win a benchmark, but it built the right thing and the benchmark reflects it. The product controls real desktops, real browsers, and real terminals. Not a sandboxed browser tab. Not an API wrapper pretending to be an agent. Actual computer use across the full surface area of a modern work environment. The agent swarm architecture means you can run parallel execution across multiple tasks simultaneously, which is the thing that takes automation from 'nice to have' to 'this replaced three headcount.' There's a desktop app for direct use, cloud VMs for scalable deployment, and BYOK support if you need to keep your data inside your own infrastructure. The free tier is real and usable, not a 5-minute demo. If you're a developer, the BYOK option means you're not locked into Coasty's pricing forever. You bring your own keys, you control your costs. That's a level of honesty about the product that most AI vendors are too scared to offer.