AI API Cost Calculator

AI API costs are calculated by multiplying your total input and output token throughput by the vendor's published rates per 1,000,000 tokens. Providers like OpenAI, Anthropic, Google, and DeepSeek bill input prompt processing and output text generation at separate rates, with output tokens costing 3x to 4x more due to sequential generation.

Input tokens are processed in parallel during prompt encoding, allowing GPUs to process large context windows rapidly. Output completion tokens are generated autoregressively—one token at a time—where each new token requires a full forward pass through the model weights. This sequential GPU compute makes output tokens far more resource-intensive to produce.

You can reduce API costs by shortening system prompt context, caching repetitive prompt headers (prompt caching), routing routine tasks to lightweight models like GPT-4o-mini or Claude Haiku (model cascading), and using asynchronous Batch APIs which offer up to 50% discount on non-urgent jobs.

Prompt caching allows AI providers to store reusable prompt prefixes—such as long system instructions, documentation context, or codebases—in memory across requests. Subsequent requests that reuse the cached prefix receive a 50% to 90% discount on input token pricing, dramatically cutting operating costs for context-heavy applications.

Proprietary APIs (like GPT-4o or Claude 3.5) charge strictly per token with zero infrastructure management. Open-source models (like Llama 3 or DeepSeek V3) hosted on cloud providers (like Together AI or Fireworks) offer lower per-token rates at high volume, but self-hosting on dedicated GPU instances requires paying fixed hourly server costs regardless of traffic.

Our database is updated regularly based on official pricing changes published by OpenAI, Anthropic, Google Cloud, DeepSeek, Meta, and major open-source cloud hosting platforms. All calculations reflect current per-million token rates for input, output, cached input, and batch workloads.