> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mobilerun.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Credits

> Understand how credits work and what they are used for on Mobilerun.

Credits are the currency used to pay for usage on Mobilerun. 1 credit = \$0.01 USD. You receive credits with your device subscriptions or can top up your balance as needed.

## How Credits Work

Credits are consumed when you use Mobilerun services. Your credit balance decreases as you run tasks.

## Credit Usage

### Agent Execution (LLM Tokens)

Each action the agent takes during task execution consumes credits based on the LLM tokens used. On average, agent steps cost approximately **\~0.5 credits per step**.

The exact cost per step varies depending on the model you select, the length of the conversation context, and whether features like vision or reasoning are enabled. You can monitor your LLM credit consumption in the Billing section of the dashboard.

## Getting Credits

### Device Subscriptions

Each device subscription includes a monthly credit allocation that refreshes every billing cycle:

| Device                                         | Monthly Credits | Price       |
| ---------------------------------------------- | --------------- | ----------- |
| [Personal Phone](/device-types#personal-phone) | 250             | \$5/month   |
| [Cloud Phone](/device-types#cloud-phone)       | 2,500           | \$50/month  |
| [Physical Phone](/device-types#physical-phone) | 5,000           | \$150/month |

No base plan is required — sign up for free and add devices as needed.

### Top Up

Purchase additional credits when you need more capacity:

* **\$5 per 500 credits** (one-time purchase)
* Credits are added to your balance immediately
* Top-up credits do not expire

## Monitoring Usage

Track your credit consumption in the Billing section of the dashboard:

* Current credit balance
* Usage breakdown by category
* Historical consumption trends

## Cost Optimization

| Strategy                      | Description                                                  |
| ----------------------------- | ------------------------------------------------------------ |
| **Select appropriate models** | Use faster, cheaper models for simple tasks                  |
| **Optimize prompts**          | Well-written prompts reduce the number of agent steps needed |