AI Agent Models in GoHighLevel: Choose the Right AI for Every Workflow

AI Agent Models in GoHighLevel now give you access to three major providers inside the AI Agent Action: Anthropic, Google, and OpenAI.

That gives you a much wider range of options when building workflows. Instead of choosing from one provider, you can now pick from several models designed for different levels of speed, reasoning, and complexity.

Anthropic options include Claude Opus 5, Claude Sonnet 5, and Claude Haiku 4.5. Google adds Gemini 3.6 Flash and Gemini 3.1 Pro Preview. OpenAI remains available with GPT-5.6 Luna, GPT-5.6 Tera, GPT-5.6 Sol, and GPT-5 Nano.

GoHighLevel has also redesigned the model picker. Models are grouped by provider, making it easier to compare your options without digging through a long, mixed list.

Models that support deeper reasoning are marked with a thinking chip. Each model also includes a short description, giving you a better idea of what it’s built to handle before you select it.

The other big change is effort control. On supported AI Agent Models, you can choose Low, Medium, or High reasoning effort from the same dropdown.

Low effort can help with faster, simpler jobs. Medium gives you more balance. High is better suited to tasks where deeper reasoning and accuracy matter more.

Put together, these changes give you far more control over how each AI Agent Action performs inside your workflows.

AI Agent Models matter because not every workflow task needs the same level of AI power. Some jobs are simple. Others need deeper reasoning, better context handling, and more careful decision-making.

For example, a basic intent classification step may only need a fast, lightweight model. A more complex task, like reviewing several pieces of contact data before deciding the next action, may benefit from a stronger model with higher reasoning effort.

This gives agencies more control over how they build AI-powered workflows. Instead of using the most powerful option for everything, you can match the model to the job. That can help you avoid wasting resources on simple tasks while still giving important actions the extra reasoning they need.

The reasoning effort setting adds another layer of control. Low, Medium, and High settings let you decide how much thinking a supported model should apply before returning a result.

That means you can balance speed, depth, and token usage on each AI Agent Action independently. For agencies running lots of workflows across multiple client accounts, that flexibility can make a real difference.

In simple terms, you now have more ways to fine-tune how AI works inside GHL. That can lead to faster workflows, better outputs, and smarter use of your AI budget.

Using AI Agent Models in GoHighLevel starts inside the AI Agent Action in a workflow. You’ll open the workflow, access the AI Agent Action, choose your provider and model, set the reasoning effort when supported, then save and publish your changes.

The important part is choosing the model and reasoning level that fit the job. A simple classification task may only need a faster model with lower reasoning, while a more complex task may benefit from a stronger model and higher reasoning effort. Here are the steps to configure AI Agent Models inside a GoHighLevel workflow.

• Access the Workflows page in GoHighLevel
• Access the AI Agent Action
• Select the AI Provider and Model
• Set the Reasoning Effort
• Save and Publish the Workflow

To start make sure you are logged in to your GoHighLevel sub-account.

The Main Menu on the left side of your screen includes all the main areas you work in when using GHL.

1.1 Click the Automation main menu item.

• Inside the Automation section you will find Workflows, Overview, and other workflow tools.

1.2 Click Workflows in the top menu if the Workflow List page is not already showing.

• The Workflow List page contains the workflows available in your sub-account.
• You can open an existing workflow or create a new workflow from this page.

1.3 Click the workflow that you want to update.

• The Workflow Builder page will open.
• You will see the triggers and actions that make up that workflow.

2.1 Locate the AI Agent Action in the workflow.

• If the workflow already uses an AI Agent Action, find that action in the workflow path.
• If you are building a new workflow, add an AI Agent Action in the position where you want the AI task to run.

2.2 Click the AI Agent Action.

• The AI Agent Action section will open so you can review and edit its settings.
• Review the existing instructions or prompt before changing the model. Your model choice should match the job you are asking the AI Agent to perform.

3.1 Click the Model dropdown inside the AI Agent Action.

• The redesigned model selection section will open.
• Available models are grouped by provider so you can compare Anthropic, Google, and OpenAI options.

3.2 Review the available provider groups.

• Under Anthropic, you can review the available Claude models.
• Under Google, you can review the available Gemini models.
• Under OpenAI, you can review the available GPT models.

3.3 Review the model description before making your selection.

• Each model includes a short description explaining what it is designed to handle.
• Models that support reasoning are marked with a thinking chip.

3.4 Click the model you want to use for this AI Agent Action.

• The selected model will be assigned to this individual workflow action.
• Choose the model based on the complexity of the job instead of automatically choosing the most powerful option.

4.1 Locate the Effort setting for the selected model.

• The reasoning effort options are available for models that support thinking.
• If the selected model does not support reasoning controls, these options may not appear.

4.2 Select Low, Medium, or High reasoning effort.

• Choose Low when the task is simple and faster responses are more important.
• Choose Medium when you want a balance between response speed and deeper reasoning.
• Choose High when the AI Agent needs to work through a more complex task where deeper reasoning is important.

4.3 Confirm that the selected effort level matches the task this AI Agent Action performs.

• Higher reasoning effort is not automatically better for every workflow.
• Simple tasks such as intent classification, basic reads, or straightforward operations may not need High reasoning.
• More complex tasks involving multiple decisions or detailed analysis may benefit from Medium or High reasoning.

5.1 Review the selected model and reasoning effort.

• Confirm that the AI provider, model, and effort level are correct before saving your changes.

5.2 Click Save for the AI Agent Action if the action requires saving before you return to the workflow.

• Your updated AI Agent settings will be applied to that workflow action.

5.3 Click Save or Publish for the workflow.

• This makes the updated workflow configuration available for use.

5.4 Test the workflow with a test contact or controlled example.

• Review the result produced by the AI Agent Action.
• Confirm that the response is accurate enough for the task and that the workflow behaves as expected.

5.5 Adjust the model or reasoning effort if needed.

• If the result is too basic, test a more capable model or a higher reasoning effort.
• If the task is simple but the response uses more processing than necessary, test a lighter model or lower reasoning effort.

[Include Screenshot of the completed AI Agent Action and workflow controls. Red Garage Arrow 1 points to the selected model. Red Garage Arrow 2 points to the selected reasoning effort. Red Garage Arrow 3 points to the Save or Publish control. Put a red box around the final settings that should be reviewed before publishing.]

The goal with AI Agent Models is not to use the biggest model for every task. It is to give each workflow action the amount of AI power it actually needs. That gives you more control over response quality, speed, and token usage while keeping each automation focused on the job it needs to do.

Choosing the right AI Agent Models in GoHighLevel comes down to one thing: match the model to the task. You do not need the most powerful option every time.

If the AI Agent Action is handling a simple job, start with a faster, lighter model. Good examples include intent classification, basic routing, simple data reads, or straightforward text handling.

For more complex work, move up to a stronger model. This makes more sense when the AI needs to compare information, follow several instructions, make decisions, or work through a multi-step problem.

Reasoning effort works the same way.

Use Low when speed matters and the task is simple. This can be a good fit for high-volume workflow actions where you only need a short, clear result.

Use Medium when you want a balance between speed and deeper thinking. This is a strong starting point for tasks that involve some judgment but are not highly complex.

Use High when the AI Agent needs to work through a more difficult task where accuracy and deeper reasoning matter more than response speed.

The key is to test. Run the same task with different AI Agent Models and reasoning levels, then compare the output.

If a lighter model gives you the result you need, use it. If the output is weak or inconsistent, move up to a stronger model or increase the reasoning effort.

That approach helps you avoid wasting processing power while still giving important workflow actions the extra intelligence they need.

Getting more value from AI Agent Models does not mean always choosing the strongest option. The better approach is to use the lightest model that can do the job well, then increase capability only when the workflow needs it.

Start simple. If the AI Agent Action only needs to classify intent, read basic information, or complete a straightforward task, test a faster model first. This can help keep response times down and avoid using more resources than necessary.

Use higher reasoning effort only when the task actually needs deeper thinking. A High setting can make sense for more complex decisions, but it may be unnecessary for simple actions that only need a clear yes-or-no result.

Test the same prompt across different AI Agent Models before rolling it out across client accounts. Small differences in model behavior can have a big impact when a workflow runs hundreds or thousands of times.

Keep your AI Agent instructions clear and specific. Even a strong model can produce poor results if the prompt is vague. Tell the AI exactly what it should review, what decision it should make, and what type of output you expect.

It is also smart to separate different jobs into different AI Agent Actions. One action can handle a simple classification task with a lighter model, while another can use stronger reasoning for a more complex decision.

Finally, keep an eye on results after making changes. GoHighLevel has already added token optimization to the AI Agent Action, and this new model control gives you another way to balance quality, speed, and usage.

The best setup is the one that gives you reliable results without using more AI power than the task requires.

AI Agent Models give agencies more control over how AI is used inside each workflow. Instead of treating every task the same, you can now match the model and reasoning level to the job.

That can make your automations more efficient. A fast, lighter model can handle high-volume tasks like lead classification or simple routing, while a stronger model can handle more complex decisions where context and accuracy matter more.

For example, you could use one AI Agent Action to classify an incoming lead, then use another AI Agent Action later in the workflow to review the lead’s details and decide the best next step. Each action can use a different model and reasoning level.

That gives you a more practical way to control AI usage across client accounts. You are not forced to use the same level of processing for every step in the workflow.

For agencies managing multiple sub-accounts, that flexibility can add up. Better model selection can help you balance output quality, workflow speed, and token usage across a larger number of automations.

It also makes AI-powered workflows easier to scale. You can build a clear process for when to use a lighter model, when to use deeper reasoning, and when a task needs a stronger AI option.

The real advantage is control. You can build smarter workflows around the task, instead of forcing every task to fit the same AI setup.

AI Agent Models give GoHighLevel users more control over how AI works inside workflows.

You can now choose between Anthropic, Google, and OpenAI models, then match the model to the task instead of using the same setup everywhere.

The new reasoning effort controls add another useful layer. Low, Medium, and High settings let you balance speed, deeper thinking, and token usage based on what each AI Agent Action actually needs.

For agencies, that means more flexibility. You can use faster models for simple, high-volume tasks and stronger reasoning for more complex decisions where accuracy matters more.

The key is to test before rolling changes across client accounts. Compare models, review the output, and use the simplest setup that consistently gives you the result you need.

This update makes the AI Agent Action more practical, more flexible, and easier to fine-tune for real business workflows.

Have you tested any of the new AI Agent Models yet? Which provider are you planning to try first?