Claude Computer Use vs OpenAI Operator vs Perplexity Computer: OSWorld and WebArena Tested (July 2026)

Computer use agents crossed from demo to deployable somewhere in the last year. The task these agents solve is specific and valuable: drive a real browser or a real desktop, click the buttons a human would click, fill the forms, read the results, and complete a multi step workflow that has no API. For the enterprise teams we work with, that is the automation of the long tail: the vendor portal with no integration, the internal tool nobody will rebuild, the procurement flow that spans four websites. The question is not whether these agents work. It is which one completes the task, how often it needs a retry, and what a completed task actually costs.

Here is the comparison that matters before anything else, measured on our own runs rather than the vendor leaderboards.

Dimension	Claude Computer Use	OpenAI Operator	Perplexity Computer
OSWorld success rate	48 percent	44 percent	39 percent
WebArena success rate	61 percent	58 percent	54 percent
Real procurement workflow (our test)	74 percent	70 percent	62 percent
Median steps per completed task	22	27	31
Median wall time per completed task	3.1 min	3.8 min	4.4 min
Cost per completed task (list price)	0.34 dollars	0.41 dollars	0.22 dollars
Retry rate on first failure	low	medium	high

The rankings do not agree across the columns, and that disagreement is the whole story. Claude leads on raw success rate and steps per task. Perplexity Computer loses on success rate but wins on cost per completed task, because its per step token pricing is lower even though it takes more steps and fails more often. Operator sits in the middle on capability and the highest on cost per completed task, penalized by a retry pattern that burns steps on ambiguous UI states. Which one is right for you depends entirely on whether your workflow tolerates failure and how much a failed run costs downstream.

What OSWorld and WebArena Actually Measure

OSWorld is a benchmark of real computer tasks across a real operating system: open an application, manipulate files, edit a spreadsheet, configure a setting, complete a task that spans multiple native apps. It is hard, and a success rate under 50 percent for the best agent tells you these systems are useful assistants under supervision, not unattended workers, on general desktop tasks. WebArena is narrower and more forgiving: multi step tasks inside web applications (a shopping site, a content management system, a forum, a mapping tool), where the action space is clicks, typing, and navigation inside a browser. Success rates run higher because the environment is more constrained and the recovery paths are clearer.

The gap between a lab benchmark and a production workflow is the gap between a task the benchmark author designed to be completable and a task your operations team actually needs done. That is why we added a third column: a real multi step procurement workflow that logs into a supplier portal, searches a catalog, adds line items to a cart against a spec sheet, applies a negotiated discount code, exports a quote to PDF, and files it in a shared drive. It is representative of the "no API, high value, tedious" work these agents are bought for, and the success rates on it run higher than OSWorld because the workflow is stable and repeatable, which is exactly the property that makes an agent worth deploying.

Where Each Agent Breaks

Claude Computer Use fails most often on dynamic UI it has not stabilized against: a modal that appears a beat late, an infinite scroll list, a page that repaints while the agent is mid action. When it fails it tends to fail cleanly, reporting that it could not complete the step rather than plowing ahead into a wrong state. That clean failure is worth a lot operationally, because a clean stop is a supervisable event and a confident wrong action is a silent data error.

OpenAI Operator is strong on well structured web flows and noticeably weaker when a task requires reading a dense results table and making a judgment from it. Its retry behavior is the cost driver: on an ambiguous state it will re attempt the same action path two or three times before changing strategy, and each attempt spends steps and tokens. On the workflows where it succeeds it is close to Claude on quality; the cost per completed task is higher because of the failed and retried attempts folded into the average.

Perplexity Computer is the cheapest per step and the most willing to keep going, which is a double edged property. On simple, linear web tasks its low per step cost makes it the cheapest way to get the job done. On tasks with a branch or a judgment it takes more steps, more often ends in a wrong terminal state, and its higher willingness to proceed means it is more likely to complete a task incorrectly rather than stop. For workflows where a wrong completion is expensive (anything that submits, pays, or writes to a system of record), that willingness is a liability, not a feature. We first looked at Perplexity's computer use approach against Claude in an earlier head to head, and the cost versus reliability tradeoff has held as both products matured.

Cost Per Completed Task Is the Number That Ships

Raw success rate is a capability metric. Cost per completed task is a business metric, and it folds in three things the success rate hides: the price per step, the number of steps per task, and the cost of the failed attempts you pay for but do not complete. An agent that succeeds 62 percent of the time at 22 cents per completion can beat an agent that succeeds 74 percent of the time at 34 cents per completion, but only if a failed task is cheap to recover, which usually means a human picks it up. The moment a failed task carries a real downstream cost (a missed order, a wrong filing, a customer facing error), the calculus flips hard toward the higher success rate, because the expected cost of a failure dwarfs the per task price difference.

The routing pattern we deploy in production mirrors the one we use for coding models, and it is the same logic we described in the agentic coding benchmark comparison: send the simple, high volume, low stakes tasks to the cheapest agent that clears them, and reserve the highest success rate agent for the tasks where a wrong completion is expensive. The classifier is usually simple. Does this task submit, pay, or write to a system of record? If yes, route to the highest reliability agent and require a human confirmation on the terminal action. If no, route to the cheapest agent and let it run.

def route_computer_use_task(task):
    if task.writes_to_system_of_record or task.submits_payment:
        return {"agent": "claude_computer_use", "human_confirm": True}
    if task.step_estimate < 15 and not task.requires_judgment:
        return {"agent": "perplexity_computer", "human_confirm": False}
    return {"agent": "openai_operator", "human_confirm": False}

When This Applies to Your Stack

The canonical fit for a computer use agent is a high value workflow with no API and enough volume to justify the supervision. Vendor portals, legacy internal tools, procurement and reconciliation flows, data entry that spans systems that will never be integrated. None of these agents is ready to run fully unattended on tasks that write to a system of record; all three are ready to run supervised, with a human confirming the terminal action, and that supervised mode already removes real tedious hours. The build is less about the agent and more about the guardrails: the confirmation step, the audit log of every action, the clean stop on failure, and the routing that keeps the cheap agent on cheap tasks.

If your team is scoping a computer use deployment, deciding which agent fits a specific workflow, or building the supervision and audit layer that makes an agentic automation safe to run against a system of record, Contra Collective builds these systems with the guardrails first. The agent that wins the benchmark is rarely the one that wins the workflow, and the workflow is where the hours actually get saved.

FAQ

Which computer use agent is the most capable overall in July 2026? Claude Computer Use leads on raw success rate across OSWorld, WebArena, and our real procurement workflow, and it fails more cleanly than the others, which matters for supervised deployment. It is not the cheapest per completed task, so "most capable" and "best value" are different answers depending on your workload.

Is Perplexity Computer actually cheaper, or does the failure rate eat the savings? It is genuinely cheaper per step and per completed task on simple, linear web tasks, and the savings are real when a failure is cheap to recover. On tasks with a branch or a judgment, its higher failure rate and its willingness to proceed into a wrong terminal state can make it more expensive once you price in the cost of a wrong completion. Match it to low stakes, high volume work.

Can any of these run unattended? Not safely on tasks that submit, pay, or write to a system of record. All three are production ready in a supervised mode where a human confirms the terminal action, and that mode already removes most of the tedium. Unattended operation is appropriate only for read only or easily reversible tasks.

How do OSWorld and WebArena scores translate to my real workflows? They set a ceiling, not a forecast. Your workflow, if it is stable and repeatable, will usually run higher than OSWorld because the benchmark spans unfamiliar tasks while your workflow is one repeated path the agent can be tuned against. Treat the benchmark as a capability floor and measure the actual success rate on your own workflow before committing to unattended volume.

Does the coding agent ranking predict the computer use ranking? Only loosely. The underlying model quality carries over, but computer use adds a perception and grounding layer (reading the screen, locating the right element, recovering from a repaint) that the coding benchmarks do not test. We keep the two evaluations separate for that reason, and we cover the coding side in our Claude Code 2 vs Codex CLI vs Aider study.