LLM Cost Calculator

LLM APIs charge per token — a token is roughly 4 characters or 0.75 words in English. Costs are split into input tokens (your prompt) and output tokens (the model's response). Input is usually cheaper than output. Monthly cost = (input tokens × input price + output tokens × output price) × daily requests × 30.

Input tokens are the text you send to the model (your prompt, system message, conversation history). Output tokens are the tokens the model generates in its response. Output tokens typically cost 2-5× more than input tokens. For long conversations with history, input costs can dominate.

A short prompt (50 words) ≈ 65 tokens. A medium article (500 words) ≈ 650 tokens. A long document (2,000 words) ≈ 2,600 tokens. Rule of thumb: 1 token ≈ 0.75 words. Use model-specific tokenizers (tiktoken for OpenAI, Claude's token counter) for exact counts.

It depends on task complexity. For simple tasks (classification, extraction, summarization): Gemini Flash or GPT-4o Mini are cheapest. For complex reasoning, code, or analysis: Claude Sonnet or GPT-4o offer the best quality-to-cost ratio. For maximum quality: GPT-4o or Claude Opus. Always benchmark quality before optimizing purely for cost.

1) Use smaller models for simple tasks. 2) Cache common responses (prompt caching saves up to 90% on repeated prompts). 3) Reduce conversation history sent with each request. 4) Use streaming and stop generation early when complete. 5) Compress system prompts. 6) Batch requests when possible. 7) Set max_tokens to avoid runaway responses.