Prompt Token Counter

Count characters, words, and estimate tokens for LLM prompts. Useful for OpenAI, Claude, and other AI APIs.

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Count tokens for AI prompts

Models charge and truncate by tokens, not raw characters. This tool helps before you hit provider limits—state clearly whether estimates are exact per vendor tokenizer or heuristic so you avoid misleading claims.

Especificaciones Técnicas y Arquitectura

Technical Standard:
ECMA-376 / ISO/IEC 29500 (Office Open XML)
Execution Environment:
100% Client-Side Browser Sandbox
Data Privacy:
Zero Server Storage / No Tracking
Processing Speed:
Real-time local CPU execution
Cross-Platform:
Works on Windows, macOS, Linux, iOS & Android

Prompt Engineering & Token Estimation Engine

LLM utilities evaluate prompt lengths, structure system instructions, and calculate approximate token counts based on byte-pair encoding (BPE) algorithms used by OpenAI, Anthropic, and Google models. This helps prevent token budget overflows and optimizes monthly API expenditure.

1

Provide Input

Enter, paste, or upload your data into the input field above.

2

Configure & Process

The tool immediately processes your input in real time according to your chosen settings.

3

Copy or Save

Use the Copy button to copy the output to your clipboard, or download the result to your device.

Nota para desarrolladores: Counting tokens before calling expensive LLM APIs prevents unexpected bill spikes and truncated model responses.
Prompt Token Counter – Free Online Tool Screenshot
Prompt Token Counter vista previa de la interfaz

Casos de Uso Comunes

  • Trim RAG chunks before embedding cost explodes
  • Size few-shot examples to fit context windows
  • QA prompt templates before production rollouts
  • Pair with JSON extractors when models return structured output

Guías y consejos de plataforma

Token Counter for ChatGPT

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Preguntas frecuentes

Why does my count differ from OpenAI’s tokenizer?
Exact counts depend on the model’s Byte-Pair Encoding or SentencePiece vocabulary. Use vendor tooling when billing precision matters; use this page for quick ballparks.
Tokens vs words?
English averages roughly 0.75 tokens per word but code and rare tokens skew higher. Measure real prompts from your product, not averages alone.