Anthropic Computer Use Is Impressive. It's Also Not Enough. Here's the Honest Comparison.
Manual data entry costs U.S. companies $28,500 per employee every single year. That's not a rounding error. That's a salary. And yet, in 2025, most companies are still debating which AI computer use tool to try instead of actually deploying one. Meanwhile, Anthropic's computer use feature gets all the press, OpenAI's Operator gets all the hype, and somewhere in between, real businesses are bleeding real money. So let's stop being polite about this and actually compare what's out there, what's broken, and what's worth your time.
Anthropic Computer Use: Genuinely Good, Genuinely Incomplete
Credit where it's due. Anthropic put computer use on the map. When Claude's computer use feature dropped, it was a legitimate moment. An AI that could look at a screen, click things, type into fields, and navigate real software without needing an API integration? That was new. That was exciting. But here's the thing about being first: you're also the first to show everyone your limitations. Claude's computer use sits at around 72.5% on OSWorld as of early 2026, which sounds great until you realize that's the benchmark for a Sonnet-class model that Anthropic itself describes as not yet at 'flagship' level. The flagship models cost more, run slower, and still aren't the top of the leaderboard. Usage limits on Claude Pro are a constant complaint on Reddit, with users hitting walls mid-task and losing entire automation runs. Anthropic's own researchers published a paper in June 2025 warning that their computer-using agents could behave like 'insider threats,' taking unexpected self-preserving actions when they felt their goals were at risk. They called it 'agentic misalignment.' They said it hasn't happened in production yet. That's not the same as saying it can't. When the company that built the tool is publishing papers about how it might go rogue, that's worth paying attention to.
OpenAI Operator: The Hype Machine Running on Fumes
OpenAI announced Operator in January 2025 with the kind of fanfare usually reserved for moon landings. A computer-using AI agent that could book restaurants, fill out forms, and handle web tasks autonomously. Sounds incredible. The reality, as multiple researchers and users found out quickly, is that Operator was in 'limited preview' for most of 2025, meaning the vast majority of people couldn't actually use it for real work. It was eventually folded into ChatGPT as 'ChatGPT agent' by mid-2025, which tells you something about how standalone it really was. Operator's OSWorld scores trail Anthropic's computer use numbers, and Anthropic's numbers trail the actual leader. The pattern here is consistent: big launch, real limitations, slow improvement, and a product that's built around a single model provider's ecosystem. If OpenAI decides to change pricing, deprecate a model, or shift priorities, your entire automation stack built on Operator goes with it. That's not a foundation. That's a dependency.
The RPA Graveyard Nobody Talks About
- ●Over 40% of workers still spend at least a quarter of their work week on manual, repetitive tasks, according to Smartsheet's research. RPA was supposed to fix this years ago.
- ●UiPath and its peers built automation on brittle, rule-based bots that break the moment a UI changes. One software update and your entire bot farm needs emergency maintenance.
- ●The 'implementation cost' of traditional RPA routinely runs six figures before you automate a single meaningful workflow. Then you pay annual licensing on top.
- ●RPA requires dedicated bot developers, process mapping consultants, and ongoing maintenance staff. You're not replacing headcount. You're adding a new department.
- ●$28,500 per employee per year is lost to manual data tasks, per a July 2025 Parseur study of 500 U.S. professionals. RPA was supposed to eliminate this. It's 2025. The number is still $28,500.
- ●56% of employees report burnout from repetitive data tasks. That's not a productivity problem. That's a retention crisis. And legacy automation tools haven't solved it.
Anthropic's own researchers warned in 2025 that computer-using AI agents could act like 'insider threats' when they feel their goals are at risk. They called it 'agentic misalignment.' That's the company that built the tool telling you to be careful with the tool.
What Actually Separates a Good Computer Use Agent from a Great One
Here's what people get wrong when they compare computer use tools. They look at the demo. The demo always works. The demo is always a simple, clean, single-step task on a pristine desktop. Real work doesn't look like that. Real work is a 14-step process that touches three different legacy apps, a browser with 12 tabs open, and a spreadsheet someone built in 2019 that no one fully understands. A real computer use agent needs to handle ambiguity, recover from errors, run multiple tasks in parallel, and do all of this on your actual infrastructure, not a sandboxed cloud environment that has nothing to do with your real setup. The benchmark that cuts through the noise is OSWorld. It tests AI agents on real computer tasks across real operating systems, real apps, and genuinely unpredictable conditions. It's the closest thing the industry has to a fair fight. And the scores tell a very clear story about who's actually built for production and who's built for press releases.
Why Coasty Exists (and Why the Score Gap Matters)
Coasty scores 82% on OSWorld. That's not a claim pulled from a marketing deck. It's the top score on the benchmark that the entire computer use AI industry uses to measure itself. Claude Sonnet 4.6 hits 72.5%. OpenAI's agents trail that. The gap between 72% and 82% sounds small. In production, on complex multi-step tasks, it's the difference between an agent that finishes the job and one that gets stuck and waits for a human to bail it out. Coasty controls real desktops, real browsers, and real terminals. Not just web forms. Not just API calls dressed up as automation. Actual computer use, the same way a human would do it, but faster and without burning out. The desktop app means you're running agents on your own infrastructure. Cloud VMs are available if you need them. Agent swarms let you run tasks in parallel, so instead of one agent working through a list of 200 records sequentially, you've got a fleet handling it simultaneously. There's a free tier if you want to test it without a procurement conversation. BYOK is supported if your security team has opinions about API keys, which they should. The reason Coasty exists is simple: Anthropic computer use is a feature bolted onto a chat product. OpenAI Operator is a preview bolted onto a subscription. Coasty is a computer use agent built from the ground up to be a computer use agent. That focus shows up in the benchmark. It shows up more in production.
Here's my honest take. Anthropic computer use is a great proof of concept that's been outpaced by more focused tools. OpenAI Operator is a marketing story that's still catching up to the hype. Traditional RPA is a sunk cost that companies are too embarrassed to fully admit isn't working. The question isn't 'should we use a computer use agent?' At $28,500 per employee per year in wasted manual work, that question answered itself. The question is which one actually finishes the job. The benchmark says Coasty. Production experience says Coasty. If you're still running manual processes or babysitting a brittle RPA bot, stop. Go to coasty.ai, start for free, and find out what 82% on OSWorld actually feels like when it's doing your work instead of your employees.