Compare real-world API costs across Claude, GPT-4o, and Gemini with our interactive API calculator. Input your projected request volumes and token distributions to forecast monthly inference spending before deploying to production.
By Zach Bailey
Standard LLM pricing is calculated per 1 million tokens ($/1M tokens). Input tokens (what you send) cost 3x to 5x less than output tokens (what the model generates). For high-scale agentic pipelines and wide context windows, prompt caching can cut input token costs by up to 90%. Use the interactive API calculator below to model monthly token consumption across top foundation models.
Adjust inputs to see our recommendation.
Building a production AI feature requires accurate financial forecasting. This API calculator removes the complexity of cross-provider pricing tables, giving you an instantaneous comparison based on your exact token ratios and volume patterns.
Finding the right model isn't just about the lowest raw price-per-token. It's about "Effective Cost" — the total amount you pay to get a deterministic, high-quality output without needing multiple retry loops or hallucination fixes.
Use a high-speed reflex model (such as Claude Haiku 5.5 or GPT-6 Luna) for intent classification and prompt triage. Escalate to heavy reasoning tiers (Claude Sonnet 5.5, Opus 5.5, or GPT-6 Terra) only when architectural complexity or formal verification demands it.
Modern frontier APIs offer KV prompt caching. When reusing static developer instructions, system prompts, or RAG reference documents, cached input tokens are billed at up to 90% off standard rates.
An API calculator calculates expected monthly AI inference expenditure based on request volume and input/output token counts per request. By applying per-million token pricing rates ($/1M tokens) across top providers (OpenAI, Anthropic, Google), it models realistic operational costs for engineering teams.
Input tokens are the characters and instructions you supply in your prompt, including system instructions and background context. Output tokens are generated sequentially by the AI model during inference. Output tokens require significantly more compute time per token, costing 3x to 5x more than input tokens.
Prompt caching allows AI providers to reuse GPU memory state for repeating prompt prefixes. For workflows with massive system instructions or codebase contexts, caching can discount input token billing by 80% to 90%, drastically lowering your effective monthly bill.
For lightweight, high-volume tasks, GPT-4o-mini ($0.15/M in) and Gemini 1.5 Flash ($0.075/M in) provide the highest throughput per dollar. For production software engineering and deep multi-step reasoning, Claude 3.5 Sonnet ($3.00/M in, $15.00/M out) remains the premier industry benchmark.