How to Calculate and Compare LLM API Token Pricing Across OpenAI, Anthropic, and Gemini

When building production AI applications, API token costs can quickly scale across input prompts, cached context windows, and reasoning output tokens. Understanding how providers meter per-million token rates helps engineering teams forecast monthly spend accurately before deployment.

1. Comparing Input, Cached, and Output Token Rates

Modern foundation models split billing into distinct tiers: standard input tokens, prompt-cached input tokens, and completion output tokens. Using an interactive AI Credits API pricing calculator allows developers to benchmark side-by-side costs across OpenAI, Anthropic Claude, Google Gemini, DeepSeek, and open-weight models in real time.

2. Practical Steps to Optimize Monthly API Spend

• Route tasks by complexity: Use lightweight flash or mini models for classification and extraction while reserving frontier reasoning models for complex synthesis.
• Enable prompt caching: Reuse static system instructions and long document contexts to cut repeated input token charges.
• Audit token budgets before launch: Estimate per-request and monthly batch costs at https://ai-credits.ai/ to prevent billing surprises.