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

# genai_input_cost

> This page explains how to use the genai_input_cost function in APL.

The `genai_input_cost` function calculates the cost of input tokens (prompt tokens) for a GenAI API call based on the model name and number of input tokens. This helps you understand and track the cost of prompts separately from responses.

You can use this function to analyze prompt costs, optimize prompt engineering for cost efficiency, track input spending separately, or create detailed cost breakdowns.

## For users of other query languages

If you come from other query languages, this section explains how to adjust your existing queries to achieve the same results in APL.

<AccordionGroup>
  <Accordion title="Splunk SPL users">
    In Splunk SPL, you would need to lookup pricing and calculate costs manually.

    <CodeGroup>
      ```sql Splunk example theme={null}
      | lookup model_pricing model OUTPUT input_price
      | eval input_cost=(input_tokens * input_price / 1000000)
      ```

      ```kusto APL equivalent theme={null}
      ['ai-logs']
      | extend input_cost = genai_input_cost(model, input_tokens)
      ```
    </CodeGroup>
  </Accordion>

  <Accordion title="ANSI SQL users">
    In ANSI SQL, you would join with a pricing table and calculate input costs.

    <CodeGroup>
      ```sql SQL example theme={null}
      SELECT 
        l.*,
        (l.input_tokens * p.input_price / 1000000) as input_cost
      FROM ai_logs l
      JOIN model_pricing p ON l.model = p.model_name
      ```

      ```kusto APL equivalent theme={null}
      ['ai-logs']
      | extend input_cost = genai_input_cost(model, input_tokens)
      ```
    </CodeGroup>
  </Accordion>
</AccordionGroup>

## Usage

### Syntax

```kusto theme={null}
genai_input_cost(model, input_tokens)
```

### Parameters

| Name          | Type   | Required | Description                                                                        |
| ------------- | ------ | -------- | ---------------------------------------------------------------------------------- |
| model         | string | Yes      | The name of the AI model (for example, 'gpt-4', 'claude-3-opus', 'gpt-3.5-turbo'). |
| input\_tokens | long   | Yes      | The number of input tokens (prompt tokens) used in the API call.                   |

### Returns

Returns a real number representing the cost in dollars (USD) for the input tokens based on the model's pricing.

## Example

Calculate the cost of input tokens for a GenAI chat operation.

**Query**

```kusto theme={null}
['otel-demo-genai']
| extend model = ['attributes.gen_ai.request.model']
| extend input_tokens = tolong(['attributes.gen_ai.usage.input_tokens'])
| extend input_cost = genai_input_cost(model, input_tokens)
| summarize total_input_cost = sum(input_cost), avg_input_cost = avg(input_cost)
```

[Run in Playground](https://play.axiom.co/axiom-play-qf1k/query?initForm=%7B%22apl%22%3A%22%5B%27otel-demo-genai%27%5D%20%7C%20extend%20model%20%3D%20%5B%27attributes.gen_ai.request.model%27%5D%20%7C%20extend%20input_tokens%20%3D%20tolong\(%5B%27attributes.gen_ai.usage.input_tokens%27%5D\)%20%7C%20extend%20input_cost%20%3D%20genai_input_cost\(model%2C%20input_tokens\)%20%7C%20summarize%20total_input_cost%20%3D%20sum\(input_cost\)%2C%20avg_input_cost%20%3D%20avg\(input_cost\)%22%7D)

**Output**

| total\_input\_cost | avg\_input\_cost |
| ------------------ | ---------------- |
| 45.67              | 0.0187           |

This query calculates the total and average cost of input tokens, helping you understand prompt spending patterns.

## List of related functions

* [genai\_output\_cost](/apl/scalar-functions/genai-functions/genai-output-cost): Calculates output token cost. Use this alongside input costs to understand the full cost breakdown.
* [genai\_cost](/apl/scalar-functions/genai-functions/genai-cost): Calculates total cost (input + output). Use this when you need combined costs.
* [genai\_get\_pricing](/apl/scalar-functions/genai-functions/genai-get-pricing): Gets pricing information. Use this to understand the pricing structure behind cost calculations.
* [genai\_estimate\_tokens](/apl/scalar-functions/genai-functions/genai-estimate-tokens): Estimates token count from text. Combine with input cost to predict prompt costs before API calls.