Prompt Token Counter
Count characters, words, and estimate tokens for LLM prompts. Useful for OpenAI, Claude, and other AI APIs.
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- ✓ Execução 100% no Navegador: Seus dados nunca saem do seu dispositivo nem tocam servidores remotos.
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- ✓ Em Conformidade com Padrões: Construído estritamente segundo padrões web RFC, ISO e 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.
Especificações Técnicas e Arquitetura
- 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.
Provide Input
Enter, paste, or upload your data into the input field above.
Configure & Process
The tool immediately processes your input in real time according to your chosen settings.
Copy or Save
Use the Copy button to copy the output to your clipboard, or download the result to your device.
Casos de Uso Comuns
- 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
Guias e dicas
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Outras Ferramentas de IA e LLM
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System Prompt Generator
Generate system prompts for LLMs. Role, task, constraints, output format. For ChatGPT, Claude, etc.
Few-Shot Prompt Builder
Build few-shot prompts with input-output examples. Structure examples for in-context learning.
Prompt Variable Extractor
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Perguntas Frequentes
- 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.