Compared with Claude and ChatGPT computer use
Claude’s computer use tool, OpenAI’s computer tool and ChatGPT agent let a frontier model operate a computer: it looks at a screenshot, decides on an action, and looks again. They are built to work out a task nobody has scripted, and they do that well. Kasm Session Runtime is built for a known task that has to run the same way every time, inside a session your organisation already runs, with a record a reviewer can check.
Either vendor’s agent can also drive a Kasm session through the runtime; see Using them together.
Side by side
Section titled “Side by side”Checked against vendor documentation on 2026-09-26. Both vendors change these products often; the sources are listed under the table.
| Claude computer use (API) | OpenAI computer tool (API) | ChatGPT agent | Kasm Session Runtime | |
|---|---|---|---|---|
| Who hosts the desktop | you: your application runs each action in your own environment; the reference implementation is a Docker container | you: “You provide the environment and execute the model’s requests” | OpenAI: a virtual browser on a remote computer in the cloud | your Kasm deployment: the runtime runs inside the session container |
| Where the model runs, which models | Claude models on the Claude API and Google Cloud (generally available), and on Claude Platform on AWS, Amazon Bedrock and Microsoft Foundry (beta) | OpenAI models through the Responses API; the guide names gpt-5.6-sol for the computer tool | OpenAI, inside ChatGPT, on paid plans | any OpenAI-compatible endpoint, local or hosted, or none; an outside agent brings its own model |
| Applications in reach | whatever runs in the desktop you provide | browser and desktop interfaces in the environment you provide | websites in its virtual browser, plus connected apps as data sources | any application on the session’s X11 display, including native apps with no DOM |
| How an action is chosen | from screenshots, as pixel coordinates; a separate browser use tool also reads a page’s accessibility tree and elements | structured mouse and keyboard actions from screenshots, or code the model writes with a library such as PyAutoGUI or Playwright | from screenshots of its virtual browser window | DOM action or key chord first, then declared targets, then accessibility, then a vision-guided click that must be verified |
| Model call per step, or replay | a model call per turn; one turn can return a short batch of actions | a model call per turn; one call can batch actions, and code execution can combine actions, loops and conditions in one call | each run is a new agent invocation, including scheduled repeats | scripted steps and compiled plans replay with no model call; the loop runs only if a postcondition fails |
| How a step is checked | the model looks at a new screenshot; the docs suggest prompting it to check each step | the model looks at a new screenshot; the guide says to check the actual outcome, not only the model’s final answer | screenshots let the agent notice errors and adjust | postconditions read back from the browser, the application, the X server or the process table; a model’s judgement only where nothing else can observe the effect |
| What is recorded | what your application keeps | what your application keeps | chats, browsing history and screenshots in the conversation history until deleted | report.json, report.md, trace.jsonl and screenshots, with the backend and rung used for each step |
| Who decides pass or fail | the loop ends when the model stops asking for actions; whether the task succeeded is for your code | your code, which the guide says should verify the result in the application | the agent reports; the person checks the result | window and process probes decide whether the application launched and stayed up, and postconditions decide each step; a model can fail a check it was asked to make but cannot turn a failure into a pass |
| What leaves the customer’s network | screenshots and tool results, sent to the model provider’s API | screenshots and tool results, sent to OpenAI’s API | the task runs in OpenAI’s cloud; sites see its traffic | nothing, with a local model; with a hosted model, the screenshots it is asked to judge; with an outside agent, what that agent asks for |
Sources, read on 2026-09-26:
- Anthropic, Computer use tool and Browser use tool
- OpenAI, Computer use
- OpenAI Help Center, ChatGPT agent and Using cloud browser in ChatGPT
Design assumptions
Section titled “Design assumptions”A computer-use tool gives a strong model the most general interface available, a screen and a mouse, and leaves the rest to the model. For a task nobody has written down, that is the right design. The model reads the screen afresh each turn, adapts to what it finds, needs no knowledge of the application in advance, and works on anything with a screen.
The runtime assumes the task is known and has to be done the same way on the hundredth run as on the first, often with a model small enough to run on the customer’s own hardware. It therefore uses the most exact channel the application offers before falling back to pixels, checks each step against what the application reports, replays a task without a model once it has been worked out, and keeps the verdict away from the model.
Strengths of computer use
Section titled “Strengths of computer use”- Open-ended tasks nobody has scripted. A frontier model reasoning on every step handles surprises that a plan or a small local model does not.
- No setup. ChatGPT agent needs a paid plan and a prompt. The API tools need an environment you build, but no knowledge of the application.
- Safeguards around the model. Anthropic runs classifiers over screenshots to flag possible prompt injection. ChatGPT agent asks for confirmation before consequential actions, lets the person take over the browser, and does not capture screenshots while they do.
- The web from a hosted browser. ChatGPT agent reaches public and signed-in websites with nothing to deploy.
Choosing between them
Section titled “Choosing between them”| You want | Use |
|---|---|
| A one-off task on the web, done for you, with nothing to deploy | ChatGPT agent |
| A frontier model working out a new task in a desktop you control | Claude computer use or OpenAI’s computer tool, in your own environment |
| The same task run unattended, many times, with a record and a deterministic verdict | the runtime: a scenario, or a plan compiled from one run |
| Automation where screens and data cannot leave your network | the runtime with a local model |
| A frontier agent’s reasoning, in a Kasm session, with checks and a record | your agent driving the session through the runtime |
Using them together
Section titled “Using them together”An agent can drive a Kasm session through the runtime’s MCP or HTTP interface instead of a raw screenshot-and-click loop. Claude Code connects as an MCP client, and OpenAI’s guide notes that an application which already exposes UI operations through function calling or remote MCP tools can keep that interface. The agent keeps its own reasoning. From the runtime it gets structured ways in (page elements by name, accessibility controls, processes, files), pixels when those run out, checks that read back from the application, and one report row per call, all in a session the customer already operates.
That setup needs no model in the runtime. See Using it with your own agent.