Prompt Token Counter
Count characters, words, and estimate tokens for LLM prompts. Useful for OpenAI, Claude, and other AI APIs.
主な特徴とプライバシー保証
- ✓ 100%ブラウザ内完結: データは端末外に出ず、外部サーバーに送信されることはありません。
- ✓ リアルタイム高速出力: ネットワーク遅延ゼロ、入力と同時に即時反映・高速コピー。
- ✓ 永久無料＆制限なし: 会員登録不要、課金なし、1日の利用制限も一切ありません。
- ✓ 標準規格準拠: RFC、ISO、W3Cウェブ標準仕様に厳密に準拠して設計されています。
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.
技術仕様とアーキテクチャ
- 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.
主な利用シーン
- 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
活用ガイドとヒント
関連のおすすめツール
連携ツール・次のステップ
Prompt Token Counterと合わせてよく使われる便利な開発者ユーティリティ
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Extract JSON from LLM responses. Parse code blocks and clean malformed JSON from AI output.
MCP Tool Schema Generator
Generate Model Context Protocol (MCP) tool definitions. Create name, description, and inputSchema JSON for MCP servers.
MCP Config Formatter
Format and validate MCP server configuration (mcp.json). Pretty-print and check structure for Cursor, Claude, and other MCP clients.
その他 AI・LLM関連ツール
LLM JSON Extractor
Extract JSON from LLM responses. Parse code blocks and clean malformed JSON from AI output.
MCP Tool Schema Generator
Generate Model Context Protocol (MCP) tool definitions. Create name, description, and inputSchema JSON for MCP servers.
MCP Config Formatter
Format and validate MCP server configuration (mcp.json). Pretty-print and check structure for Cursor, Claude, and other MCP clients.
llms.txt Generator
Generate llms.txt for your website. Creates AI-readable discovery file with name, summary, contact, and services. Spec 1.1.1 compatible.
Prompt Template Builder
Build prompts with {{variable}} placeholders. Fill in values and preview. For reusable AI prompts.
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
Extract {{variables}} or {placeholders} from prompt templates. List placeholders for documentation.
よくある質問
- 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.