Best Multimodal AI Models for Computer Use Agents 2026

GPT-5.6 Sol is the best overall model for computer-use agents in 2026, but its normalized model-only cost is $0.40 for a task that meters 50,000 input tokens and 5,000 output tokens. Gemini 3.7 Flash costs $0.05625 on the same token shape, while DeepSeek V4-Flash-Vision-Exp costs $0.0143 to $0.0286. Buy the route that minimizes cost per accepted completion, not the lowest token line.

The shortlist at a glance

The four routes below solve different versions of computer use. GPT-5.6 Sol, Claude Opus 5, and Gemini 3.7 Flash expose a computer-use tool with a defined action loop. DeepSeek V4-Flash-Vision-Exp supplies the visual model and generic tool calling, but your application must define the computer actions, executor, approvals, and recovery logic.

Prices were verified on August 22, 2026. “Starting price” means direct API list price per million tokens, shown as input/output. It excludes executor hosting, tool fees, retries, and human review unless noted.

The ranking:

1. GPT-5.6 Sol is the best overall choice when a failed action is expensive and you want the strongest disclosed computer-use result in this set.
2. Claude Opus 5 is the better enterprise control choice when browser structure, automatic prompt-injection confirmation, data handling, or regulated-workload eligibility matters more than a benchmark lead.
3. Gemini 3.7 Flash is the best-value native route for reversible browser, mobile, and desktop automation. The model is GA, but Computer Use is still Preview.
4. DeepSeek V4-Flash-Vision-Exp is the cost floor for a technical team that already owns an executor and evaluation stack. It is an experimental vision core, not a packaged computer-use system.

No one should buy a computer-use model because it can identify a button in a screenshot. A production agent must perceive the state, propose a permitted action, execute it in an isolated environment, observe the result, recover from errors, and stop for approval before consequences. The “best model” is the route that completes that full loop at the lowest acceptable risk.

The verdict in one minute

Choose GPT-5.6 Sol first for high-value work where one missed state, wrong coordinate, or failed recovery can consume several minutes of an operator's time. OpenAI reports a 62.6% result on OSWorld 2.0 for Sol. That is vendor-reported benchmark evidence, not a promise that your CRM, claims portal, or desktop app will behave the same way, but it is the strongest disclosed computer-use number among these four current routes.

Choose Claude Opus 5 when the control layer is part of the purchase. Anthropic's current toolset supplies 17 client-side actions, can batch several actions into one turn, adds a browser-specific tool that reads page structure, and automatically checks screenshots for suspected prompt injection. Computer use is now GA on the Claude API, which removes a meaningful procurement objection, although your application still owns the environment and action execution.

Choose Gemini 3.7 Flash for a reversible workflow where a native action schema matters and cost has to stay close to the floor. Its normalized token cost is far below Sol's on the workload used here. Google also exposes action intent and safety decisions, useful signals for approval and logging. The tradeoff is status: Gemini 3.7 Flash is GA, but the Computer Use capability remains Preview and Google warns against sensitive or irreversible use without close supervision.

Choose DeepSeek V4-Flash-Vision-Exp only if “build the missing control plane” sounds like an advantage. Its peak token price is still below Gemini's current Standard rate, and 100 maximum-size screenshot inputs cost no more than $0.016896 at peak before text and output. The low price is meaningful. So is the missing first-party computer action tool. A team without an existing browser or desktop harness should treat the engineering and safety work as the product, not as setup.

The 22-second test: why token price is not task cost

A screenshot agent is a loop, not one inference. The application sends a screenshot and state. The model returns one action or a short action batch. Your executor performs it. The application captures the new state and asks again. Every turn can add image tokens, tool definitions, action results, latency, and another chance to recover.

That makes a model's posted input price a floor. The buying metric is:

Accepted-task cost = model tokens + tool fees + executor runtime + human review + retries, divided by accepted completions.

“Accepted” matters. A task is not complete because the model says it is done. The output must meet a deterministic check or a named human standard. For form entry, that could mean every field matches the source record and the final submission remains behind an approval. For visual QA, it could mean the target flow completed, the expected element appeared, and the evidence screenshot was retained.

To make the API prices comparable, use one normalized task with 50,000 total metered input tokens and 5,000 output tokens. The input allowance includes screenshots, tool definitions, prior state, and results. It is not a prediction of every workflow. It is a common ruler.

• GPT-5.6 Sol costs $0.40 in model tokens.
• Claude Opus 5 costs $0.375.
• Gemini 3.7 Flash Standard Paid costs $0.05625 through December 31, 2026.
• DeepSeek V4-Flash-Vision-Exp costs $0.0143 off-peak or $0.0286 peak for cache-miss input.

Across 1,000 submitted tasks, those floors become $400 for Sol, $375 for Opus 5, $56.25 for Gemini, and $14.30 to $28.60 for DeepSeek. Those numbers exclude the executor and any provider-specific tool charges. They also assume the same metered token shape, not the same success rate.

Now add labor. At a loaded operator cost of $60 per hour, one minute costs $1. Sol's $0.3714 model premium over DeepSeek's peak rate equals 22.284 seconds of labor. Claude's premium equals 20.784 seconds. Gemini's equals 1.659 seconds.

That is the 22-second test: if Sol prevents 22 seconds of review, rework, or failed-loop recovery per submitted task, it erases its token premium over DeepSeek peak pricing. At 1,000 tasks, Sol costs $371.40 more in model tokens and needs to save about 6.19 operator hours to break even. One intervention often takes longer than that once a person opens a log, understands the state, corrects it, and restarts the run.

The test does not prove Sol will save those seconds. It tells you what your evaluation must measure. A benchmark score can justify a pilot. Only your recovery-time log can justify the budget.

The provider-specific extras change the floor:

• If an OpenAI task produces 20 billable computer-tool calls, the current $2.50-per-1,000-call price adds $0.05 before executor hosting.
• Claude's default Opus 5 computer toolset adds about 4,520 input tokens to an uncached request, worth $0.0226 at the base input rate. The browser toolset's roughly 6,610 tokens are worth $0.03305. Prompt caching can reduce repeated overhead, but you should read the metered usage instead of assuming every turn hits cache.
• Gemini charges Computer Use at the selected model's regular token rates, including output and thinking tokens. A long deliberation can make the output side larger than the simple 5,000-token ruler.
• DeepSeek caps each image at 384 input tokens, but your custom action schema, executor responses, retries, and safety checks still consume money and engineering time outside that image line.

For broader price routing, the cheapest AI API comparison covers models that do not need to drive a screen. Computer use deserves its own budget because screenshots, state transitions, and recoveries compound across turns.

1. GPT-5.6 Sol: best overall for costly failures

GPT-5.6 Sol is OpenAI's frontier multimodal model and the best overall starting point when computer-use failures carry a meaningful recovery cost. The gpt-5.6 alias routes to Sol, and the model accepts image input, supports a 1,050,000-token context window, and can use the Responses API's current computer tool.

OpenAI reports 62.6% on OSWorld 2.0 for Sol, compared with 50.2% for Terra and 45.6% for Luna. OSWorld measures an agent operating desktop software, which is closer to this buying decision than a general chat benchmark. The result still came from OpenAI's evaluation setup. Treat it as a reason to put Sol in the pilot, not permission to skip your own acceptance tests.

What Sol does well

The useful part of OpenAI's current integration is flexibility. The model can inspect screenshots and return computer actions through the built-in tool, or it can work through custom tools and a code-execution harness. That lets a technical team start with a Playwright browser and move to a full virtual machine when a workflow crosses native desktop apps.

The current guide also gives the safety layer the right priority. It recommends an isolated browser or VM, an empty inherited environment, disabled extensions and local filesystem access where possible, and explicit approval for consequential actions. It tells the agent to stop when on-screen content resembles prompt injection. These are implementation instructions, not automatic protections, but they push a pilot toward a safer default.

The strongest use case is a valuable, reversible workflow with messy interfaces. Think of a mid-market finance team that must collect data from three web portals, reconcile it against a source record, and prepare an entry for approval. Sol's premium makes sense if stronger state tracking and recovery avoid even a small amount of operator cleanup. It makes less sense for a clean form whose controls never move and whose data could be sent through an API.

The OpenAI price book

OpenAI has three current GPT-5.6 tiers that support computer use:

• GPT-5.6 Sol: $5.00 per million input tokens, $0.50 per million cached input tokens, and $30.00 per million output tokens.
• GPT-5.6 Terra: $2.00 per million input tokens, $0.20 per million cached input tokens, and $12.00 per million output tokens.
• GPT-5.6 Luna: $0.20 per million input tokens, $0.02 per million cached input tokens, and $1.20 per million output tokens.

The computer tool can add $2.50 per 1,000 billable tool calls, while tokens consumed by built-in tools are billed at the selected model's rate. Sol has no supported API Free tier.

The family creates a useful routing ladder. Establish the quality ceiling with Sol. Re-run the same accepted task set on Terra. Move a task class to Terra only when its completion and review profile remains inside the threshold. Luna is a much cheaper worker, but its provider-reported OSWorld result is lower. It belongs on bounded, reversible work after the acceptance rule is proven, not as the default merely because its input tokens are far cheaper than Sol's.

A safe Sol pilot

1. 1

   Choose one bounded workflow

   Pick a task with a clear start state, a deterministic finish check, and no irreversible action before approval. Good candidates include browser QA, evidence collection, and preparing a record for review. Avoid purchasing, deleting, publishing, or submitting during the first evaluation.

2. 2

   Freeze the environment

   Use an isolated browser profile, container, or VM. Remove inherited environment variables, disable extensions and local filesystem access where possible, allowlist the required domains, and expose only the credentials the task needs.

3. 3

   Define approval before execution

   Write the actions that always require a person, such as sending a message, accepting terms, changing account data, or making a financial commitment. The executor, not the model's prose, enforces the pause.

4. 4

   Log state and acceptance

   Retain the initial state, each screenshot, proposed action, executed action, tool result, safety stop, final state, and acceptance result. A final model message is not an acceptance check.

5. 5

   Route down only after Sol passes

   Use Sol to establish the achievable completion and review baseline. Then compare Terra and Luna on the same tasks, same environment, and same approval rules. Keep Sol as the escalation for the task shapes that fail below it.

Best for: High-value, multi-step browser or desktop work where failed recovery is expensive.
Standout: OpenAI reports 62.6% on OSWorld 2.0, the strongest disclosed current computer-use result in this set.
Pricing: Sol $5/$30, Terra $2/$12, and Luna $0.20/$1.20 per million input/output tokens, plus applicable tool-call charges.
Free trial: No supported API Free tier for Sol.

Who should skip it: Skip Sol as the default worker when a deterministic API exists, when the task is low value and extremely repetitive, or when Terra, Luna, or Gemini meets the same acceptance threshold. Do not pay the frontier rate to click a stable control that normal automation can address more safely.

2. Claude Opus 5: best for governed enterprise desktop work

Claude Opus 5 is the best choice when the computer-use control layer matters as much as raw model quality. Anthropic made computer use generally available on the Claude API on August 20, 2026, alongside a new browser use tool. The current computer_toolset_20260801 exposes 17 client-side tools through one declaration and requires no beta header.

The distinction between computer use and browser use is practical. Computer use works from screenshots and controls a full desktop through mouse and keyboard actions. Browser use also reads page structure and acts on specific page elements, a better fit when the workflow stays inside web applications. Structure can make a field or button easier to target than screen coordinates alone.

Both remain client-side execution tools. Claude does not connect to the desktop itself. Your application runs each action inside a container, VM, or controlled browser, returns the result, and calls the model again. Computer use is not currently available inside Claude Managed Agents, so a buyer who expects Anthropic to host the complete desktop session has picked the wrong product boundary.

Why Claude earns the governance pick

Claude can return several computer actions in one model turn. Your executor runs those actions sequentially and stops at the first failure. That can remove repeated model round trips for a safe sequence such as click, type, and observe. It does not justify batching through a state that needs inspection. If a click might open the wrong account, the agent should see the result before it types.

Anthropic also runs screenshot classifiers that can identify suspected prompt injection and steer the model to request confirmation before the next action. Customers can contact support to opt out, but the default is useful for workflows that browse untrusted pages. The classifier is another defense layer, not a substitute for domain allowlists, credential boundaries, or executor-level approval.

The data story is clearer than it was during beta. The client controls storage for screenshots, actions, and files, and Anthropic says computer use is eligible for zero data retention. Computer use is also eligible for HIPAA-regulated workloads under Anthropic's BAA. Eligibility does not make an application compliant by itself, but it can remove a model-level blocker for a healthcare procurement review.

That combination fits a claims, insurance, finance, or operations workflow where the system must reach software without a useful API and where the buyer needs evidence for every action. Anthropic cites one customer, Asteroid, reporting that its longest claims workflow fell from 32 minutes to 13 minutes, task cost dropped about 30%, and completion reached 100% without prompt changes. That is an attributed customer result, not an expected rate for a new deployment. It shows the consequence worth measuring: fewer loop turns and better targeting can change the operating cost, not merely the model score.

The deeper Claude multi-action cost analysis explains when an action batch saves money and when observation between actions is still mandatory.

The Claude price book

The current toolset supports Sonnet 5, Opus 5, Fable 5, limited-availability Mythos 5, and Opus 4.8. The relevant current model ladder is:

• Claude Sonnet 5: $2 per million base input tokens and $10 per million output tokens. A 5-minute cache write costs $2.50, a 1-hour cache write costs $4, and a cache hit costs $0.20 per million tokens.
• Claude Opus 5: $5 per million base input tokens and $25 per million output tokens. A 5-minute cache write costs $6.25, a 1-hour cache write costs $10, and a cache hit costs $0.50 per million tokens.
• Claude Fable 5 and Claude Mythos 5: $10 per million base input tokens and $50 per million output tokens. Mythos 5 has limited availability.
• Opus 5 Fast mode: $10 per million input tokens and $50 per million output tokens.
• Opus 5 Batch: $2.50 per million input tokens and $12.50 per million output tokens. Batch is useful for asynchronous model work, but an interactive computer-use loop still has to observe and act in sequence.

New API users receive a small, unspecified amount of free credits. Anthropic asks enterprise buyers to contact sales for an extended trial.

The hidden line is tool definition overhead. The default computer toolset adds about 4,520 input tokens on Opus 5 and 4,590 on Sonnet 5 to a request. Disabling zoom removes roughly 410 tokens. The browser toolset is larger, adding about 6,610 tokens on Opus 5 and 6,670 on Sonnet 5, while enabling all four optional browser members adds about 880 tokens.

At the Opus 5 base input rate, one uncached default computer toolset costs about $0.0226 before the screenshot, task context, action result, or output. The browser toolset costs about $0.03305 on the same basis. A short task can spend more on declaring the available control surface than on its business data. Cache stable instructions and tool definitions when the workflow permits, disable members you do not use, and trust the response's metered usage rather than an estimate.

Best for: Enterprise browser and desktop workflows that need explicit controls, audit evidence, and a supported migration path.
Standout: GA computer use with 17 client-side actions, sequential action batches, and a separate page-structure-aware browser tool.
Pricing: Sonnet 5 $2/$10, Opus 5 $5/$25, and Fable 5 or limited Mythos 5 $10/$50 per million base input/output tokens.
Free trial: Small new-user API credits, amount not stated; enterprise evaluation requires sales contact.

Who should skip it: Skip full desktop computer use when the task stays inside a webpage and browser use covers it. Skip both when a stable API is available. A buyer whose only goal is the cheapest reversible UI loop should start with Gemini; a buyer who already owns the entire action harness should price DeepSeek as the model core.

3. Gemini 3.7 Flash: best-value native computer use

Gemini 3.7 Flash is Google's recommended model for Computer Use and the best-value route here with a vendor-defined action loop. The model itself is GA with a 1 million-token context window and a 64,000-token maximum output. Computer Use remains Preview.

Google supports three target environments: browser, mobile, and desktop. The application sends the prompt, environment, and screenshot. Gemini returns a function call for an interface action. Your client scales the coordinates, executes the action, captures the new screen, and sends it back. A secure VM or container and a client-side action handler are still required, with Google's examples using Playwright.

Gemini 3.x adds two signals that are especially useful for an approval layer. Each action can carry an intent, a short explanation of why the model chose it. A separate safety decision can mark the action allowed, require confirmation, or blocked. Intent is not proof that an action is correct, but it gives your policy engine and reviewer more context than a bare click coordinate.

The safety system is configurable by policy category, and screenshot prompt-injection detection is opt-in. This makes Gemini attractive for a product team building its own approval experience. The application can show the proposed action, model intent, policy decision, and current screenshot together, then require a person only where the workflow's risk warrants it.

Where Gemini fits

Gemini is the sensible first pilot for reversible cross-environment work. A mobile product team could use the same model route to exercise a signup flow on a web browser and a mobile build, collect screenshots, and flag where the journey diverges. A support-operations team could use it to gather public information from approved sites and prepare a record without pressing the final submit button.

At the normalized token shape, Gemini's current Standard Paid floor is $0.05625 per task or $56.25 per 1,000 tasks. It costs $0.02765 more per task than DeepSeek at peak. At $60 per hour, Gemini needs to save only 1.659 seconds of engineering or review time to cover that model premium. A defined action schema, safety decision, or avoided custom adapter can clear that bar quickly.

The warning is status, not fine print. Google says Computer Use may contain errors and security vulnerabilities and advises against critical decisions, sensitive data, or serious irreversible actions without close supervision. A GA model does not turn a Preview tool into a GA capability. Procurement, risk review, and rollout scope should use the tool's status.

The Gemini price book

Google lists four processing modes for Gemini 3.7 Flash. Through December 31, 2026:

• Standard: $0.75 per million input tokens, $3.75 per million output tokens, and $0.075 per million cached tokens.
• Batch: $0.375 per million input tokens, $1.875 per million output tokens, and $0.0375 per million cached tokens.
• Flex: $0.375 per million input tokens, $1.875 per million output tokens, and $0.0375 per million cached tokens.
• Priority: $1.35 per million input tokens, $6.75 per million output tokens, and $0.135 per million cached tokens.

Starting January 1, 2027, Standard becomes $1.50/$7.50 with $0.15 cached input. Batch and Flex become $0.75/$3.75 with $0.075 cached input. Priority becomes $2.70/$13.50 with $0.27 cached input.

The model's Standard Free tier offers free input and output tokens, but Computer Use is not available on the Free tier. Google bills Computer Use as regular model tokens on Paid. That distinction belongs in any trial plan: experimenting with the base model in AI Studio is not a free production test of the computer-use loop.

An interactive agent will usually care about Standard or Priority rather than the asynchronous economics implied by Batch. Flex can be attractive for schedulable work if its service behavior fits the loop. Do not copy the cheapest mode into a business case without confirming that the end-to-end interaction pattern and latency work for the job.

Best for: Reversible browser, mobile, and desktop automation that needs a native action schema at a low token price.
Standout: One recommended model across three environments, with action intent, configurable safety policies, confirmation decisions, and optional prompt-injection detection.
Pricing: Standard $0.75/$3.75 through December 31, 2026; Batch and Flex $0.375/$1.875; Priority $1.35/$6.75 per million input/output tokens.
Free trial: The model has a Free tier, but Computer Use is Paid-only.

Who should skip it: Skip Gemini Computer Use for a regulated or irreversible production workflow that cannot accept Preview risk. Skip it when the browser alone is not enough and the target desktop environment cannot be isolated. If quality failures consume more than a small amount of operator time, compare Sol and Claude before optimizing the token line.

4. DeepSeek V4-Flash-Vision-Exp: best for custom budget harnesses

DeepSeek V4-Flash-Vision-Exp is the cheapest model core in this ranking and the least complete computer-use product. DeepSeek released the experimental multimodal model on August 21, 2026 under the exact API identifier deepseek-v4-flash-vision-exp.

The model accepts text and images, supports generic tool calls, and works through OpenAI-compatible Chat Completions and Responses APIs plus an Anthropic-compatible interface. It has a 1 million-token context window, a maximum 384,000-token output, thinking and non-thinking modes, and a listed concurrency limit of 2,500.

Those are useful ingredients for a computer-use agent. They are not a first-party computer-use action tool. DeepSeek documents how to send screenshots and how to call generic tools, but it does not document a built-in browser or desktop action schema, an executor, approval decisions, or an operating environment. Calling it a model core is an editorial inference from that product boundary.

In practice, your team must define tools such as click, type, scroll, screenshot, wait, and ask for approval. It must validate arguments, map coordinates, run actions in an isolated browser or VM, return the new state, detect loops, stop on errors, and log everything. A mature automation team may prefer that control. A team buying its first computer-use agent should count it as application development.

Why the price deserves attention

DeepSeek prices V4-Flash-Vision-Exp at the same peak and off-peak schedule shown for the Flash route:

• Off-peak: $0.007 per million cache-hit input tokens, $0.22 per million cache-miss input tokens, and $0.66 per million output tokens.
• Peak: $0.014 per million cache-hit input tokens, $0.44 per million cache-miss input tokens, and $1.32 per million output tokens.

Peak hours are 01:00 to 04:00 UTC and 06:00 to 10:00 UTC. All other hours are off-peak. Starting August 23, 2026 Beijing time, off-peak rates apply throughout Saturdays and Sundays in Beijing time.

The pricing page does not advertise a free tier or trial. The practical evaluation cost is still tiny. At the normalized 50,000-input/5,000-output shape, the model costs $0.0143 off-peak or $0.0286 peak. A team can afford substantial experimental volume before tokens become the main line item. That does not make development, review, or a bad action cheap.

The vision token rule is unusually clear. Large images are resized to roughly an 800 by 800-pixel budget, with each image capped at 384 input tokens. At peak cache-miss pricing, 100 maximum-token screenshots cost no more than $0.016896 in image input. Off-peak, they cost $0.008448. Text, outputs, repeated history, generic tool calls, and retries remain outside that calculation.

This cap is both a cost advantage and a perception limit. A dense spreadsheet, small dialog, or multi-column dashboard can lose important detail when compressed into the model's image budget. Send a focused crop or use the documented original detail path when necessary, then verify how the service resizes it. Cheap image tokens do not guarantee accurate tiny-text recognition.

DeepSeek accepts JPEG, PNG, GIF, and WebP. You can send base64 data, a public URL, or a Files API reference. The service allows up to 600 images per request, a maximum of 8,192 pixels per side, and 4,096 pixels per side when the request contains 15 or more images. Base64 and URL images can be up to 32 MiB, Files API images up to 64 MiB, and the inline request body up to 48 MiB.

Those ceilings are far above a normal screenshot loop, so the binding wall is not upload count. It is the custom control plane. The team must prove that its action schema, coordinate mapping, approvals, and recovery logic produce accepted outcomes. A model that can see a button has not earned permission to press it.

For a broader model comparison, DeepSeek vs ChatGPT covers the vendors beyond screen-driving workflows.

Best for: Experienced agent teams that want a low-cost vision core inside their own browser or desktop harness.
Standout: Up to 384 input tokens per image and $0.22/$0.66 off-peak cache-miss input/output pricing.
Pricing: $0.22/$0.66 off-peak or $0.44/$1.32 peak per million cache-miss input/output tokens; cache hits cost $0.007 off-peak or $0.014 peak.
Free trial: No free tier or trial advertised on the live pricing page.

Who should skip it: Skip DeepSeek when the buyer expects a ready action loop, needs a vendor-defined safety decision, or lacks an isolated executor and evaluation system. Also skip it for tiny-text or dense-screen tasks until focused screenshots have passed a representative visual test.

Who should pick what

A funded founder automating reversible competitive research should start with Gemini 3.7 Flash. It provides the action loop, covers browser and desktop environments, and keeps the model floor low enough to run a broad evaluation. The choice flips to Sol if review and failed-loop recovery consume more than the roughly 22-second premium test suggests. It flips to Claude if untrusted web content and approval design become the harder problem.

A mid-market CTO moving data through a regulated or legacy application should start with Claude Opus 5 or Sonnet 5. Computer use is GA on the Claude API, screenshot classifiers can request confirmation, the client controls the environment, and the feature is ZDR eligible. HIPAA eligibility under a BAA matters for the model review, but the workflow still needs least-privilege credentials, protected logs, and a human before submission. Choose browser use instead of full computer use when every step stays on webpages.

A senior operator automating a high-value, messy desktop workflow should start with GPT-5.6 Sol. Sol's provider-reported OSWorld lead and the small labor break-even justify paying for the quality ceiling first. Once the acceptance bar is known, route the stable task shapes to Terra, Luna, or Gemini. The mistake is starting with the cheapest tier and never learning how much recovery a stronger model could remove.

A technical builder with an existing action executor, policy service, and evaluation suite should include DeepSeek V4-Flash-Vision-Exp. At that maturity level, the custom action schema is not a new project, and the off-peak rate can change the cost of large evaluation and batch workloads. Keep a stronger route for small-text screens, ambiguous state, and repeated failures.

The choice flips on four questions:

1. Is a stable API available? Use it. Screen control is the fallback for interfaces without a reliable programmatic path.
2. Can a wrong action be reversed? If not, require approval and prefer a route whose status, controls, and procurement fit the consequence.
3. Do you already own the executor? If not, DeepSeek's token saving does not buy a complete solution.
4. How many review seconds does the premium remove? Use accepted-task logs to choose Sol, Claude, Gemini, or DeepSeek. Do not use the rate card alone.

How these models were picked

This ranking uses five criteria tied to a buying decision:

• Computer-use completeness: Does the vendor define an action loop, or only provide vision and generic tool calling?
• Current evidence: Is there a relevant disclosed computer-use result, product status, or implementation detail that can be checked today?
• Accepted-task economics: What does a common token shape cost, and what human time must a premium route save?
• Production wall: Which part still belongs to the buyer, including sandboxing, executor work, approvals, prompt-injection defense, and logging?
• Price durability: Is the price standard, time-limited, peak-based, or attached to a Preview capability?

The products were compared from their live documentation, pricing pages, model pages, and named vendor case evidence on August 22, 2026. They were not exercised in a shared browser harness during this run, so this page does not claim a test result. The cost model is original analysis from the locked rate cards. The 240-run protocol below is how a buyer can produce workflow-specific evidence.

This roundup covers four routes because each represents a distinct buying decision: maximum reliability, governed enterprise operation, low-cost elasticity, or custom-stack economics. Each included route has a current differentiator, a price book, an implementation path, and a candid wall. Older models appear below only where a buyer may still encounter them during migration.

No affiliate relationship influenced this ranking, and no irrelevant commercial partner was inserted. Commercial availability did not move a model up or down.

The ones to avoid

Avoid OpenAI computer-use-preview for a new integration

OpenAI's current GA request shape uses a GPT-5.5 or newer model with tools: [{ type: "computer" }]. The older computer-use-preview model and computer_use_preview tool use a different action shape and require legacy request settings. Keep it only while migrating an existing application. Starting a new build on the legacy path creates work you already know you must replace.

Avoid Gemini 2.5 Computer Use Preview when 3.7 is available

Google labels Gemini 2.5 Computer Use Preview as the legacy model. It costs $1.25 per million input tokens and $10 per million output tokens at or below 200,000 prompt tokens, rising to $2.50/$15 above 200,000. Gemini 3.7 Flash is Google's recommended model for computer use and costs $0.75/$3.75 through December 31, 2026. Keep 2.5 only for a compatibility dependency you have measured and cannot yet remove.

Avoid text-only DeepSeek routes disguised by a vision proxy

Only deepseek-v4-flash-vision-exp accepts images on the official DeepSeek API. Other DeepSeek models return a 400 error for image input. A third-party adapter can ask another model to describe the screenshot and pass text to DeepSeek, but that evaluates a two-model stack, not DeepSeek visual computer use. Price, latency, information loss, and data handling all change. Name the proxy as a separate component or use the exact vision model.

Avoid any screen agent without an acceptance test

“The model reached the end” is not a business result. A browser agent can land on the wrong account, type into the wrong field, or stop after a page changed without recognizing the failure. If the task has no deterministic check or named reviewer standard, it is not ready for autonomous execution on any of these models.

The production checklist that matters more than the model name

The model is one component inside a controlled system. A production review should answer these questions before volume rises:

• Environment: Is the browser or desktop isolated from the host, personal accounts, local files, and unrelated credentials?
• Reach: Are domains, applications, and allowed actions restricted to the workflow?
• Secrets: Does each task receive only the credential scope it needs, with no secret exposed in screenshots or logs?
• Consequences: Which actions always require human confirmation, and does the executor enforce that rule?
• Injection: What happens when a page, image, document, or dialog tells the agent to ignore its task?
• State: Can the system prove which account, record, window, and step it is operating on before an action?
• Recovery: Does a failed action stop the batch, preserve evidence, and return enough state for a safe retry or escalation?
• Acceptance: What machine check or reviewer decision marks the task complete?
• Economics: Are model tokens, tool calls, runtime, retries, and review seconds captured per accepted completion?
• Change: Can the team replay the same task set after a model snapshot, price, browser, or target UI changes?

A model switch should be boring. The action interface, safety policy, logs, and acceptance test stay stable while the model changes behind them. If changing providers requires rewriting the entire control plane, the system has confused one vendor's tool format with its product architecture.

The Monday move: run a 240-run bake-off

Do not schedule a broad “AI agent pilot.” Pick one task class and make one routing decision next week. Use 20 representative tasks, the four routes in this ranking, and three attempts per route. That produces 240 runs. Three repetitions are enough to expose some one-off luck without pretending the sample is a universal benchmark.

1. 1

   Monday: freeze the task and acceptance rule

   Choose 20 tasks from one workflow, including normal cases, small-screen states, pop-ups, ambiguous controls, and one safe prompt-injection fixture. Remove irreversible final actions. Write the exact pass condition and the situations that require review.

2. 2

   Tuesday: hold the environment constant

   Run every route against the same viewport, browser or VM image, domain allowlist, initial state, credential scope, action vocabulary, time limit, and approval policy. For DeepSeek, map your custom tools to the same underlying executor actions used by the native routes.

3. 3

   Wednesday: collect complete run costs

   Capture metered input, cached input, output, billable tool calls, executor runtime, elapsed time, attempts, safety stops, and human review seconds. Store the first failure state and the accepted final evidence.

4. 4

   Thursday: price accepted completions

   Add model cost, tool fees, executor cost, reviewer labor at the agreed hourly rate, and retry cost. Divide by accepted completions. Also report the share accepted without edits, accepted after repair, rejected safely, and failed unsafely.

5. 5

   Friday: route one class, preserve escalation

   Choose the cheapest route that clears the acceptance and safety threshold. Keep the stronger model as an escalation for repeated action, uncertain state, suspected injection, failed validation, or high-consequence steps. Record the model identifier and price date so the decision can be replayed.

The Monday move is small on purpose. One measured task class creates a defensible budget line. A broad demo creates a collection of screenshots and no routing rule.

Frequently asked questions

Which AI is best for computer use?

GPT-5.6 Sol is the best overall starting point for costly failures, based on its disclosed 62.6% OSWorld 2.0 result and the small labor break-even on its token premium. Claude Opus 5 is the better governed enterprise route, Gemini 3.7 Flash is the value route, and DeepSeek V4-Flash-Vision-Exp fits teams that already own the action harness.

Which is the best multimodal AI?

For computer use, the best multimodal model is the one that sees the target interface well enough, works with the required action and approval layer, and minimizes cost per accepted completion. Visual reasoning alone does not provide a secure executor, a safety policy, or a completion check.

What is the most powerful AI model right now?

No single benchmark supports that claim across every task. OpenAI reports GPT-5.6 Sol at 62.6% on OSWorld 2.0, while Anthropic positions Opus 5 strongly on computer use and Google recommends Gemini 3.7 Flash for its tool. Use those disclosures to choose a shortlist, then rank the models on accepted completions, recovery time, and safety inside your own workflow.

Download the AI Tools Map for Business Owners and turn this shortlist into a one-week computer-use bake-off.