AI API Cost Calculator
Estimate your monthly API costs for OpenAI, Anthropic, Mistral, and Google models.
Budgeting for third-party LLM APIs? Our calculator helps you estimate your monthly bill based on your daily token usage. Compare costs across different models like GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro to find the most cost-effective solution for your application.
AI API Cost Calculator
Estimate your monthly API costs for major providers like OpenAI, Anthropic, and Mistral.
About This Tool
The AI API Cost Calculator is an essential tool for developers, startups, and businesses building on top of large language models. As more companies leverage powerful APIs from providers like OpenAI, Anthropic, and Google, understanding and forecasting the costs has become a critical part of financial planning. Pricing is typically based on the number of tokens processed, with different rates for input (the data you send) and output (the data the model generates). This calculator simplifies the estimation process. By inputting your expected daily token volume and the typical ratio of your inputs to outputs, you can instantly see a projected monthly bill for a wide range of popular models. This allows you to compare the cost-effectiveness of different models, budget accurately for your AI-powered features, and make informed decisions about which API best suits your performance needs and financial constraints.
How to Use This Tool
- Use the first slider to set your estimated total number of tokens (input + output) you will process per day.
- Use the second slider to set the ratio of your input tokens vs. output tokens.
- Click the "Estimate API Costs" button.
- The tool will display tables showing the estimated monthly cost for various models from OpenAI, Anthropic, Mistral, and Google.
- Compare the costs to find the most economical model for your usage pattern.
In-Depth Guide
How AI API Pricing Works: Tokens
The standard pricing model for LLM APIs is pay-as-you-go, based on tokens. A token is a piece of a word, roughly equivalent to 4 characters of English text. Providers charge you for the number of tokens in your prompt (input tokens) and the number of tokens in the model's response (output tokens). As you can see in the calculator, output tokens are almost always more expensive than input tokens. This is because generating new text is more computationally intensive for the model than reading the text you provide.
The Model Cascade Strategy for Cost Savings
A powerful strategy for managing costs is the "model cascade" or "router" approach. Instead of sending every request to the most expensive model (like GPT-4o), you first send it to a cheaper, faster model (like Claude 3 Haiku). If the cheap model provides a good answer or is confident it can handle the request, you return that response. If it fails or determines the query is too complex, you then "cascade" or "fall back" to the more expensive model. This ensures you only pay for the high-powered model when you absolutely need it, dramatically reducing average cost per query.
Choosing a Provider and Model
The "best" model changes constantly. OpenAI's GPT series is known for its strong reasoning and coding capabilities. Anthropic's Claude models are often praised for their writing style and large context windows. Mistral offers strong open-source and commercial models with competitive pricing. Google's Gemini models are tightly integrated with its ecosystem and offer a balanced performance. The best practice is to test your specific use case on multiple models to find the optimal blend of performance, latency, and cost.
Context Window Length and Cost
Another factor to consider is the "context window," which is the maximum number of tokens a model can handle in a single prompt and response. Models with very large context windows (like Claude 3 and Gemini 1.5 Pro) are excellent for processing large documents, but sending a large number of tokens, even if it fits in the window, can be expensive. Always try to be as concise as possible in your prompts to manage these costs effectively.