ChatGPT API Cost Estimator
Estimate your monthly expenditure for using the ChatGPT API based on your anticipated token usage and chosen model pricing. Understanding token costs is crucial for managing your budget when integrating large language models into your applications.
The average number of tokens in your prompts/inputs.
The average number of tokens in the model's responses/outputs.
How many API calls you expect to make daily.
The number of days you'll be using the API in a month.
Price for 1,000 input tokens (e.g., $0.0015 for gpt-3.5-turbo-0125).
Price for 1,000 output tokens (e.g., $0.0050 for gpt-3.5-turbo-0125).
Understanding ChatGPT API Costs
The ChatGPT API, provided by OpenAI, allows developers to integrate powerful language models into their own applications. Unlike a fixed subscription, the API typically operates on a pay-as-you-go model, primarily based on "tokens." This calculator helps you estimate your potential monthly costs.
What are Tokens?
Tokens are fundamental units of text that the language model processes. For English text, one token is roughly equivalent to about four characters or ¾ of a word. When you send a prompt to the API, both your input (the prompt) and the model's output (the response) are measured in tokens. Different models and different token types (input vs. output) can have varying costs.
Input vs. Output Tokens
- Input Tokens: These are the tokens you send to the API as part of your prompt or query. For example, if you ask "Write a short story about a cat," the words "Write a short story about a cat" are converted into tokens and counted as input tokens.
- Output Tokens: These are the tokens generated by the model as its response. If the model generates a 200-word story, those 200 words are converted into tokens and counted as output tokens. Generally, output tokens are more expensive than input tokens because they represent the computational effort of generating new content.
Factors Influencing Cost
Your total API cost is primarily driven by:
- Model Choice: Different models (e.g., GPT-3.5 Turbo, GPT-4) have different pricing tiers. More advanced models typically cost more per token.
- Volume of Usage: The more requests you make and the longer your inputs and outputs are, the higher your token count will be, leading to increased costs.
- Input/Output Ratio: Applications that generate very long responses from short prompts will incur higher output token costs. Conversely, applications that process large amounts of text (e.g., summarization) and produce short outputs will have higher input token costs.
How to Use This Calculator
To get an accurate estimate, consider the following:
- Average Input/Output Tokens: If you're unsure, start with typical values (e.g., 500 input, 1000 output) and adjust as you gain experience with your application's specific use cases. You can often find tokenizers online to estimate token counts for sample texts.
- Requests per Day: Estimate how frequently your application will call the API.
- Days per Month: How many days in a month will your application be active?
- Cost per 1k Tokens: Refer to OpenAI's official pricing page for the most up-to-date costs for your chosen model (e.g., gpt-3.5-turbo-0125, gpt-4-turbo). The default values in this calculator are based on common GPT-3.5 Turbo pricing at the time of writing.
Example Calculation:
Let's say you use the following parameters:
- Average Input Tokens: 500
- Average Output Tokens: 1000
- Requests per Day: 100
- Days per Month: 30
- Cost per 1k Input Tokens: $0.0015
- Cost per 1k Output Tokens: $0.0050
Monthly Input Tokens: 500 tokens/request * 100 requests/day * 30 days/month = 1,500,000 tokens
Monthly Output Tokens: 1000 tokens/request * 100 requests/day * 30 days/month = 3,000,000 tokens
Monthly Input Cost: (1,500,000 / 1000) * $0.0015 = $2.25
Monthly Output Cost: (3,000,000 / 1000) * $0.0050 = $15.00
Total Estimated Monthly Cost: $2.25 + $15.00 = $17.25
This calculator provides a useful estimate, but actual costs may vary slightly due to factors like tokenization nuances and specific API usage patterns.