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

# Clone OpenAI pricing

Manually managing per-token pricing can be complex, but with Flexprice, you can automate and scale your billing effortlessly. This guide walks you through setting up OpenAI's O1 pricing model using a **package-based pricing approach**. This method ensures clarity in billing, making it ideal for AI APIs, generative models, and machine learning services.

**Use Cases**

* AI APIs (LLMs like OpenAI, Anthropic, Mistral)

* Machine learning inference services

* Text-to-Speech or Speech-to-Text APIs

**OpenAI** charges users based on the number of input, output and cached tokens processed by their models. You can view the official pricing details here: [OpenAI Pricing](https://openai.com/api/pricing/).

The O1 model has the following pricing structure:

| **Token Type**      | **Price per Million Tokens** |
| ------------------- | ---------------------------- |
| Input Tokens        | \$15.00 per million tokens   |
| Output Tokens       | \$60.00 per million tokens   |
| Cached Input Tokens | \$7.50 per million tokens    |

For example, if a user processes:

* 5 million input tokens → $75.00 ($15.00 x 5)

* 2 million output tokens → $120.00 ($60.00 x 2)

* 1 million cached input tokens → $7.50 ($7.50 x 1)

* Total Cost = \$202.50

Now, let’s configure the pricing for the O1 model using **Flexprice**.

**Configuring Pricing of o1 model in Flexprice**

1. **Create** [Metered Features](/docs/product-catalogue/features/create) **for Token Usage**
   Since token usage is metered, we first define three separate Metered Features in Flexprice for input tokens, output tokens, and cached input tokens.

   | **Feature Name**    | **Feature Type** | **Aggregation Method** | **Key**     | **Filters**                                   |
   | ------------------- | ---------------- | ---------------------- | ----------- | --------------------------------------------- |
   | Input Tokens        | Metered          | SUM                    | model\_name | model: OpenAI O1, prompt\_type: input         |
   | Output Tokens       | Metered          | SUM                    | model\_name | model: OpenAI O1, prompt\_type: output        |
   | Cached Input Tokens | Metered          | SUM                    | model\_name | model: OpenAI O1, prompt\_type: cached\_input |

2. **Create a Plan with** [Package-Based Pricing](/docs/product-catalogue/plans/billing-models/package)
   Once the metered features are created, we define a **Plan** that charges users per million tokens rather than per individual token.

   | **Metered Feature** | **Billing Model** | Charges                    |
   | ------------------- | ----------------- | -------------------------- |
   | Input Tokens        | Package Charge    | \$15.00 per million tokens |
   | Output Tokens       | Package Charge    | \$60.00 per million tokens |
   | Cached Input Tokens | Package Charge    | \$7.50 per million tokens  |

Now, whenever a customer **purchases this plan and starts using it**, they will:

* See **real-time usage events** for token consumption.

* Get a **dynamically generated proposed invoice** based on their usage.

* Have full transparency in billing, ensuring clarity on costs.

This process ensures that **AI companies can charge users fairly based on actual usage** while providing predictable and scalable billing.