Compare real-world API costs across Claude, GPT-4o, and Gemini. Input your expected volume and token counts to find the perfect balance between reasoning quality and budget.
By Zach Bailey
Standard LLM pricing is calculated per 1 million tokens. Input tokens (what you send) are significantly cheaper than output tokens (what the AI generates). For high-scale scaling, prompt caching (available in Claude 3.5 Sonnet) can reduce input costs by up to 90%.
Adjust inputs to see our recommendation.
Building a production AI feature requires accurate financial forecasting. This tool removes the complexity of cross-provider pricing tables, giving you a direct comparison based on your specific traffic patterns.
Finding the right model isn't just about the cheapest price-per-token. It's about "Effective Cost" — the total amount you pay to get a successful, high-quality result without needing multiple retries.
Use a "small" model (like GPT-4o-mini or Haiku) for intent classification and simple routing. Only trigger the "large" model (Sonnet or Pro) when the task requires high reasoning.
Models like Claude 3.5 Sonnet offer prompt caching. If you reuse large system prompts or documents, caching can reduce your input costs by up to 90%.
Input tokens are the words or code you send to the model (the prompt). Output tokens are what the model generates back. Input tokens are typically 2-4x cheaper than output tokens across most providers.
Most direct API providers (OpenAI, Anthropic, Google) charge per-token with no bulk discounts. However, enterprise tiers or third-party aggregators may offer tiered pricing for millions of daily requests.
For simple tasks, GPT-4o-mini and Gemini 1.5 Flash currently lead the market in value. For high reasoning, Claude 3.5 Sonnet is considered the best performance-per-dollar balance.