Prompt Token Counter

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

Partager:

Fonctionnalités Clés et Garantie de Confidentialité

  • ✓ Exécution 100% Navigateur : Vos données ne quittent jamais votre appareil ni ne touchent un serveur distant.
  • ✓ Résultat Instantané en Temps Réel : Aucune latence réseau, calcul immédiat et copie rapide.
  • ✓ Gratuit et Illimité pour Toujours : Pas d'inscription, pas de paywall ni de quota quotidien.
  • ✓ Conforme aux Standards : Conçu rigoureusement selon les spécifications RFC, ISO et W3C.

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.

Spécifications Techniques et Architecture

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.

Note pour développeurs : 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 aperçu de l'interface

Cas d'Utilisation Fréquents

  • 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

Guides et conseils

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Foire aux questions

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.