Model Parameter

The model parameter lets you specify which AI model to use for individual focused_action and screenshot actions that include extract_prompt, directly in your workflow prompt.

For comprehensive documentation on model configuration, including workflow-level settings, custom configurations, and supported providers, see Model Configuration.

Syntax

Add model="Model Name" to your action:

Use focused_action with model="Sonnet 4.5 (Thinking)" to verify the order status

Take screenshot with extract_prompt="Extract invoice data" and model="Sonnet 4.5"

Using the Model Picker

In the prompt editor:

Slash menu: Type / → select “Model Override” → inserts model=""
Tab autocomplete: Type model → press Tab → inserts model=""
Direct typing: Type model="" manually

With your cursor inside the quotes, a dropdown appears showing all available models. Hover to see model details including computer use support. The dropdown also includes System Default, which clears the override and lets Cyberdesk choose the normal default for that action.

Computer Use Requirement

For focused_action: Prefer models marked as “computer use” models. The editor warns if you pick another configured model, because non-computer-use models may not handle clicking, typing, and navigation reliably.For extract_prompt: Any vision-capable model works since it only reads the screen.

What Happens If You Omit `model`

focused_action uses the workflow’s main agent model, or Cyberdesk’s main default if the workflow does not set one
extract_prompt uses Cyberdesk’s current extraction default unless you override it

Example Prompts

Using Different Models for Different Tasks

Navigate to the patient records system and log in.

Use focused_action with model="Sonnet 4.5 (Thinking)" to find the patient 
whose name is {patient_name} and MRN ends with {mrn_suffix}. This requires 
careful reasoning to match the correct record.

Once on the patient chart, take a screenshot with extract_prompt="Extract 
all vital signs as JSON: {blood_pressure, heart_rate, temperature, 
oxygen_saturation}" and model="Sonnet 4.5"

Navigate to the lab results tab and extract data from each page:
- Take screenshot with extract_prompt="Extract lab results as JSON array" 
  and model="Sonnet 4.5" and process_async="batch"
- Scroll down
- Repeat until all results captured

Cost Optimization with Model Selection

Open the invoice management portal.

Search for invoice {invoice_number} and open the details page.

Use focused_action with model="Sonnet 4.5 (Thinking)" to verify this is 
the correct invoice by checking the customer name matches {customer_name} 
and the date is {invoice_date}.

Take a screenshot with extract_prompt="Extract complete invoice data: 
invoice_number, date, customer info, all line items with descriptions 
and amounts, subtotal, tax, and total. Format as detailed JSON." 
and model="Sonnet 4.5" and process_async="run"

Continue to the next invoice while extraction runs in background.

Complex Decision-Making with Thinking Models

Navigate to the appointment scheduling system.

Use focused_action with model="Sonnet 4.5 (Thinking)" to analyze the 
calendar and find an available slot that meets ALL criteria:
- Day is {preferred_day}
- Time is between {earliest_time} and {latest_time}
- Provider is {preferred_doctor}
- Duration allows for {appointment_length} minutes

If no slot meets all criteria, use focused_action with model="Sonnet 4.5 (Thinking)" 
to find the closest alternative and explain the tradeoff.

Click to book the selected slot.

Batch Extraction with Consistent Model

Open the product catalog and navigate to category {category_name}.

For each page of products:
- Take screenshot with extract_prompt="Extract all visible products as JSON: 
  {sku, name, price, stock_status, rating}" and model="Sonnet 4.5" 
  and process_async="batch"
- Scroll to next page
- Repeat until end of catalog

All extractions use the same fast model for consistent results and cost.

Verification Steps with Reasoning

Submit the insurance claim form.

Use focused_action with model="Sonnet 4.5 (Thinking)" to analyze the 
confirmation screen:
- Verify the claim ID is displayed
- Check that all submitted fields match our input values
- Identify any warnings or additional steps required
- Save the claim ID as {{claim_id}}

If any discrepancies are found, use declare_task_failed with details.

Take screenshot with extract_prompt="Extract confirmation details: 
claim_id, submission_date, estimated_processing_time, any reference 
numbers" and model="Sonnet 4.5"

Mixed Extraction Strategy

Open the monthly financial report.

Fast clipboard extraction for key fields:
- Triple-click Report Date field, use copy_to_clipboard with key "report_date"
- Triple-click Total Revenue field, use copy_to_clipboard with key "total_revenue"

Vision extraction for complex content:
- Take screenshot of the revenue breakdown chart with extract_prompt=
  "Extract revenue by category from this chart as JSON" and model="Sonnet 4.5"

- Take screenshot of the expenses table with extract_prompt=
  "Extract all expense line items as JSON array" and model="Sonnet 4.5" 
  and process_async="batch"

Dynamic verification:
- Use focused_action with model="Sonnet 4.5 (Thinking)" to verify the totals 
  in the summary section match the detailed breakdowns

Model Selection Guidelines

Task Type	Recommended Model	Why
Complex decisions, multi-criteria matching	Thinking models (e.g., “Sonnet 4.5 (Thinking)“)	Better reasoning for nuanced decisions
Simple verification, straightforward clicks	Standard computer-use models	Faster, lower cost
Bulk data extraction	Fast vision models (e.g., “Sonnet 4.5”)	Speed and cost efficiency
Critical accuracy needs	Thinking models with verification	Reduces errors on important data

Tips

Default is Usually Fine

If you don’t specify a model, the workflow uses its configured default. Only override when you have a specific reason.

Match Model to Task

Use thinking models for complex reasoning, standard models for bulk extraction.

Check Computer Use

The model picker shows which models support computer use. Required for focused_action.

Combine with Async

Pair model= with process_async for efficient bulk extraction with your preferred model.

Model Configuration — Full details on workflow-level and per-action model settings
Focused Action — Dynamic decisions and observations
Extract Prompt — Vision-based data extraction

Getting started

SDK Guides

Cyberdriver

Workflow Prompting

Webhooks

Concepts

Additional Details

Syntax

Using the Model Picker

Computer Use Requirement

What Happens If You Omit `model`

Example Prompts

Using Different Models for Different Tasks

Cost Optimization with Model Selection

Complex Decision-Making with Thinking Models

Batch Extraction with Consistent Model

Verification Steps with Reasoning

Mixed Extraction Strategy

Model Selection Guidelines

Tips

Default is Usually Fine

Match Model to Task

Check Computer Use

Combine with Async

​Syntax

​Using the Model Picker

​Computer Use Requirement

​What Happens If You Omit model

​Example Prompts

​Using Different Models for Different Tasks

​Cost Optimization with Model Selection

​Complex Decision-Making with Thinking Models

​Batch Extraction with Consistent Model

​Verification Steps with Reasoning

​Mixed Extraction Strategy

​Model Selection Guidelines

​Tips

Default is Usually Fine

Match Model to Task

Check Computer Use

Combine with Async

​Related Documentation

Syntax

Using the Model Picker

Computer Use Requirement

What Happens If You Omit `model`

Example Prompts

Using Different Models for Different Tasks

Cost Optimization with Model Selection

Complex Decision-Making with Thinking Models

Batch Extraction with Consistent Model

Verification Steps with Reasoning

Mixed Extraction Strategy

Model Selection Guidelines

Tips

Related Documentation