Prompt Token Estimator

This tool uses a character-ratio approximation (~4 chars per token for GPT-style models, slightly different for Claude and Gemini). For exact counts, use the model's official tokenizer: tiktoken for OpenAI, Anthropic's tokenizer API, or Vertex AI for Gemini. Estimates are typically within 5–10% of actual counts.

Each model uses a different tokenizer with a different vocabulary. GPT-4/GPT-4o uses cl100k_base (100k vocabulary), Claude uses its own tokenizer with a 100k+ vocabulary, Gemini uses SentencePiece. Models with larger vocabularies tend to produce fewer tokens for the same text, especially for code and non-English text.

LLM APIs charge separately for prompt tokens (what you send) and completion tokens (what the model generates). Output tokens cost 3–5x more than input tokens because they require running the model autoregressively — each token requires a full forward pass with all KV cache from previous tokens.

1) Shorten system prompts — compress instructions without losing meaning. 2) Use cheaper models (GPT-4o mini, Gemini Flash, Claude Haiku) for simple tasks. 3) Reduce output tokens with explicit length instructions ('Answer in 3 bullets'). 4) Cache repeated prompts using prompt caching (Anthropic, OpenAI). 5) Batch similar requests. 6) Use local models (Llama, Mistral) for high-volume tasks.

This is the ratio of your total tokens (input + expected output) to the model's maximum context window. Staying under 50% is safe. Above 80%, models may degrade in quality on long-range reasoning tasks. Above 100%, the API will reject the request. If you're consistently at 80%+, consider chunking inputs or using a model with a larger context window.

For simple Q&A and classification: Gemini 1.5 Flash ($0.075/1M input) or GPT-4o mini ($0.15/1M input). For complex reasoning: compare monthly estimates here — Claude 3.5 Sonnet and GPT-4o are similarly priced at $3–$5/1M input. For bulk processing with 1M+ daily tokens: always run a cost comparison here before committing to a model.