Best Computer-Use Agents in 2026: Tested on Desktop and Browser Tasks

Computer-use agents can edit spreadsheets, organize files, navigate websites, and move information between applications.

For example, I've been using Codex to clean up folders, open apps, and troubleshoot issues on my laptop. It's become so normal that I barely think about it. I tell it what to do, and it figures out the steps. Unlike traditional automation, I don't have to script every action.

That got me curious about computer-use agents as a whole. Could other agents make navigating websites and apps feel just as effortless?

So I tested four computer-use agents on desktop tasks and then compared them to our web-agent, Tinyfish, on browser tasks.

Here's what I found.

Don't want to read the whole article? Here is the TL;DR

OpenAI Computer use agent stands out for desktop-only work. It completed all five desktop workflows, with approximately $0.47 in estimated API usage compared with $3.20 for Claude’s attempts, including retries and unsuccessful runs.

For a mix of desktop and browser work, Claude is better in our testing. It passed all four additional browser tasks, while OpenAI only fully passed two and partially passed two under our scoring rubric. That stronger browser performance makes Claude a better fit when you need one agent across both environments.

Now for browser-only workflows, Claude passed all four additional browser tests, on par with TinyFish, but our Claude API setup required us to supply the browser environment, action tools, screenshot loop, and session storage.

All that setup work is not needed with TinyFish: you just add a URL and a goal: it handles browser execution and screenshot feedback, and our saved Browser Profiles let you reuse authenticated sessions.

So if your workflow lives in the browser, just use TinyFish and automate your research, extraction, or supplier-portal tasks without building the browser infrastructure yourself.

What Are Computer-Use Agents?

Computer-use agents are AI systems that interact with software through graphical interfaces. They inspect screenshots or webpages, decide what to do next, click buttons, enter information, and check the results.

For example, you can ask an agent to correct values in a spreadsheet, save a new copy, and reopen it to verify the changes. Or ask it to find recently updated GitHub issues and return their titles, URLs, and dates.

The agent receives the goal and determines the sequence of interactions needed to reach it.

How computer-use agents work

Computer-use agents generally follow this execution loop:

1. Observe: Inspect the current screen or webpage.
2. Decide: Choose the next action based on the goal and current state.
3. Act: Click, type, scroll, or interact with an application.
4. Verify: Check whether the action worked, then continue or recover.

Visual agents use screenshots and mouse coordinates. Browser agents can also inspect the page’s DOM or accessibility tree.

Two components make this work: the model chooses actions, and the execution environment performs them and returns observations.

Computer-use agents vs. scripts and API tool calling

Desktop agents and browser agents

General-purpose computer-use agents can interact with desktop applications, local files, and websites. Browser agents specialize in workflows that take place on websites and web applications.

The four broader computer-use options I evaluated had different execution models:

I tested desktop workflows first in this comparison, then examined whether those broader capabilities were necessary for browser work.

Testing Computer-Use Agents on Desktop Tasks

To evaluate computer-use agents on various Desktop tasks, I designed five workflows covering CSV editing, file organization, document editing, cross-application reporting, and error recovery.

Claude and OpenAI completed the matched test suite.

Sai’s direct-control API returned an authorization error, while UI-TARS remained blocked by setup problems.

I tested OpenAI’s computer-use tooling through the API, not with Codex.

Desktop results

OpenAI completed all five workflows. Claude completed three, including error recovery on a recorded retry.

OpenAI completed the full desktop suite

OpenAI corrected and reopened the CSV, organized 20 files, edited a Writer document, transferred sales figures between Calc and Writer, and completed error recovery.

The cross-application task required it to calculate figures from 30 sales records, transfer them into a document, save it, and reopen it. The final output matched the expected results.

An early harness issue meant modifier keys on some clicks weren’t passed through correctly during the CSV and file-organization tests. The required workflows and saved artifacts still passed verification, so I kept the results.

Claude’s results exposed execution failures

Claude completed document editing and cross-application reporting. Its other runs showed how execution tools and verification can determine the final outcome.

In the CSV task, Claude corrected both values and saved a new file without changing the original. A token-count timeout stopped it before the final reopening check.

An executor drag error interrupted file organization. During error recovery, Claude produced the correct document on its first attempt but stopped at a local request-budget guard. Then a retry completed all five checks with no errors.

Sai and UI-TARS encountered access and setup barriers

In a separate Sai Playground test, Sai opened a CSV in LibreOffice Calc and corrected both misspelled values. It then repeatedly attempted to use Save As without producing a new file.

On a later visit, the free cloud workspace had reset and the LibreOffice setup was gone. Reinstallation timed out. Its direct-control API also returned HTTP 403, preventing the matched desktop comparison.

UI-TARS required substantial setup work. I connected UI-TARS Desktop to a UI-TARS-1.5-7B model hosted through a Hugging Face endpoint, then encountered API compatibility issues, macOS permissions, and repeated model-request timeouts.

These results describe the configurations I could access and run. Tasks that never started reliably remain untested.

Desktop costs and benchmark context

These estimates include unsuccessful attempts and retries, but exclude separately billed infrastructure.

Testing Desktop Agents Against TinyFish on Browser Tasks

I compared general-purpose computer-use agents with TinyFish on browser research, navigation, forms, and error recovery.

I then took Claude (the highest computer-use agent score in our browser tasks test) and TinyFish further into authenticated workflows and session persistence.

The benchmark repository contains all the prompts, screenshots, traces, scoring, and cost logs.

Baseline browser tasks

The first task asked agents to find five recently updated GitHub issues labeled bug, including their titles, URLs, and update dates.

Four additional tasks each had a ten-minute limit:

I checked screenshots, logs, webpages, and final browser states manually and did not rely on completion messages alone.

Baseline browser results

Claude completed all four additional tasks. It returned exact GitHub dates, filled every form field, and completed popup recovery in 36 seconds.

Its original GitHub bug task required three attempts. The first ended without a complete answer, the second reached a local budget guard, and the third returned all 20 required fields.

OpenAI retrieved the correct GitHub issues, titles, URLs, and ordering, but returned relative dates. Its 15/20 scores reflect my strict calendar-date rubric, even though the prompt allowed relative dates as a fallback.

OpenAI and Sai also lost a form point over an ambiguous instruction.

Sai retrieved all Wikipedia fields but reached the deadline on both GitHub tasks. A polling timeout interrupted popup recovery.

UI-TARS eventually clicked, typed, and navigated in the browser. Its GitHub bug run ended after seven actions and repeated model timeouts without an issue list. The remaining tasks could not start reliably.

TinyFish returned every required field for both GitHub research tasks and Wikipedia, and completed popup recovery. The original form test exposed incomplete fields despite a success message. That prompted a closer look at both the instructions and the verification.

Clarifying the form prompt improved TinyFish’s result

The message field was supposed to contain “This is a benchmark. Do not submit.” The final sentence could also be read as a separate instruction.

With clearer wording, TinyFish completed two attempts with 9/10 fields verified, in 64 and 127 seconds.

Authenticated supplier workflows: both agents completed the tasks

I also wanted to measure how they compare on something we often encounter in business workflows: work behind logged-in sessions.

Supplier portals and internal dashboards require the agent to stay authenticated while it moves between pages and completes a task. For recurring workflows, I also wanted to know whether it could reuse the session on the next run without signing in again.

So I built a synthetic supplier portal and gave TinyFish and Claude the same tasks: sign in, update a record, and confirm the change. I verified the saved results against server records.

Both completed every uninterrupted run.

Two earlier TinyFish attempts were stopped by my custom spending watchdog. I excluded them from this uninterrupted-run comparison and preserved them in the supplier-workflow repository.

The next question was what each successful implementation required me to build.

TinyFish handled the browser infrastructure

For TinyFish, I submitted a goal, URL, and saved Browser Profile. I didn't have to setup anything else.

The Claude setup needed Docker, Firefox, a GUI executor, and code to return screenshots and action results to the model.

I audited both implementations:

These figures include components and benchmark instrumentation.

Session persistence: TinyFish restored the saved browser state

I tested whether the authenticated session survived restarting the execution environment.

TinyFish reused its saved Browser Profile in a new run without logging in again.

The original Claude container lost its browser state, so I had to configure persistent Firefox storage to restore the session successfully.

Published browser benchmarks provide broader context

In a WebVoyager evaluation, TinyFish scored 91.1%, ranking #1 on the benchmark covering 641 tasks across 15 live websites. This score comes from different evaluations and grading methods but it broadens the tests beyond my tasks sample and provides useful additional context.

Choose TinyFish for Managed Browser Workflows

In my supplier-portal tests, TinyFish and Claude both completed all three uninterrupted workflows. The difference was what I had to build around them. My Claude setup required a browser environment, action tools, screenshot feedback, and persistent storage. We built TinyFish to manage that browser execution layer.

For teams automating work inside websites, that means:

• Less infrastructure to build and manage. We host the browsers and handle the action and screenshot loop. Your team can focus on defining the workflow, integrating the results, and verifying the outcome.
• Support for work behind a login. Our Vault + Profiles handle credentials and saved browser state for authenticated workflows, such as recurring supplier updates, invoice collection, and internal portal tasks.
• Execution built for the web. Our Web Agent, works through navigation, forms, filters, and pagination to work towards your goal. You provide the URL and instructions.
• A fit with your existing AI stack. Keep Claude or your preferred model for planning and reasoning. Connect TinyFish through our APIs, SDKs, or MCP so your agent can delegate browser work to us.

Start building with TinyFish by choosing one browser task your team repeats every week. Give our Web Agent the starting URL, describe the result you need, and run your first workflow in the Playground.