OpenAI API Cost Calculator

Understanding OpenAI Pricing

OpenAI charges developers based on "tokens" rather than words. A token is a chunk of a word. As a general rule of thumb, 1 token is approximately 0.75 words in English. Therefore, 100 tokens equates to roughly 75 words.

Input vs Output Tokens

Language models are typically much cheaper to "read" (Input) than they are to "write" (Output). For example, with GPT-4o, input tokens cost $5.00 per million tokens, while output tokens cost $15.00 per million tokens. This means if you have a workflow that involves summarizing massive documents into short paragraphs, your costs will be significantly lower than a workflow that requires writing long essays from short prompts.

Choosing the Right Model

• GPT-4o (Omni): OpenAI's flagship model. It is faster, cheaper, and smarter than the original GPT-4. Best for complex reasoning and coding tasks.
• GPT-4 Turbo: The predecessor to GPT-4o. Slightly more expensive but still used in many legacy systems.
• GPT-3.5 Turbo: The most cost-effective model. It is 10x cheaper than GPT-4o. Use this for simple classification tasks, basic sentiment analysis, or routing where deep reasoning isn't required.

How to Reduce LLM API Costs

If your startup's OpenAI bill is getting out of hand, consider these strategies:

1. Prompt Caching: Cache common inputs and responses so you don't repeatedly send the same data to the API.
2. Model Routing: Use a cheaper model (like GPT-3.5) to determine if a query is complex. Only pass complex queries to the expensive GPT-4o model.
3. Fine-Tuning: Instead of stuffing massive context into the prompt (few-shot prompting), fine-tune a smaller model on your dataset to reduce the input token size.