AI Prompt Token Estimator

This tool uses the standard approximation of 1 token ≈ 4 characters for English text. Code is denser — Python/JS typically runs 3–4 chars/token. For exact counts, use tiktoken (OpenAI) or the Anthropic tokenizer API. Tokenization varies slightly between providers, so use these as estimates.

Pricing reflects model capability, size, and infrastructure cost. Flagship models like GPT-4o and Claude Opus are larger and more capable but cost 10–100× more than fast/mini tiers. Choose smaller models for simple tasks (extraction, classification) and flagship models for complex reasoning, code, or long-context work.

Input tokens are the text you send (prompt, system message, conversation history). Output tokens are the response generated by the model. Output typically costs 3–5× more than input. For long streaming responses or documents, output cost dominates. Optimizing your prompts to reduce output length saves significant cost at scale.

1) Trim system prompts to essentials. 2) Use shorter examples in few-shot prompts. 3) Don't include full conversation history — summarize old context. 4) Set max_tokens to cap output. 5) Use structured output (JSON) instead of verbose prose. 6) Use prompt caching (Anthropic/OpenAI) for repeated large prompts — saves up to 90% on cached tokens.

Prompt caching stores your large prompts server-side and reuses them at a discount (Anthropic: 90% off cached input, OpenAI: 50% off). The token count is the same — you're just billed less for re-used content. This is ideal for large system prompts, documents, or RAG contexts repeated across many calls.