How to Use the LLM Token Counter & API Cost Estimator
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1
Paste your prompt or context document into the input field.
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Set expected completion tokens and projected monthly API request volume.
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Toggle prompt caching to see cost savings from automated prefix caching.
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Compare real-time per-request and monthly cost breakdowns across leading frontier models.
Practical Example & Use Case
Calculating whether switching a high-volume customer support agent from GPT-4o to DeepSeek V3 or Claude 3.5 Haiku will save $3,200 per month on 250,000 requests.
Technical Notes & Limitations
Token counts use standard subword BPE heuristic approximations (~3.85 characters per token in English). Exact tokenizer outputs may vary slightly by model byte-level BPE vocabulary.
Frequently Asked Questions
How are LLM tokens calculated?
Large language models process text in subword chunks called tokens. On average, 1,000 English words equate to roughly 1,333 tokens or approximately 4,000 characters.
How does prompt caching affect API costs?
Anthropic, OpenAI, and Google offer 50% to 90% discounts on prompt tokens that are cached across subsequent requests, dramatically slashing costs for long system prompts and RAG documents.
Are the API pricing tables kept up to date?
Yes, pricing rates reflect current published developer rates for OpenAI, Anthropic, Google Cloud Vertex/Gemini, and DeepSeek.