LLM Token Counter - Calculate Tokens for GPT-4o, Claude & Gemini
Large Language Models (LLMs) do not process raw characters or words; they operate on sub-word tokens generated by byte-pair encoding (BPE). Understanding token counts is critical for preventing context-window truncation, reducing latency, and accurately projecting production API billing. This utility calculates exact token metrics and estimated pricing across leading frontier models simultaneously.
When to Use This Tool
- API Cost Budgeting: Forecast daily and monthly cloud API costs before deploying RAG pipelines or customer chatbots.
- Context Window Enforcement: Ensure lengthy documents fit safely inside context windows (e.g. 128k for GPT-4o, 200k for Claude 3.5).
- Multilingual Token Efficiency: Inspect token inflation differences between English, Korean, and Japanese to optimize prompt translation.
- Prompt Truncation Safeguards: Prevent silent cutting of system instructions caused by oversized injected user contexts.
How to Use
- Input Prompt Text: Paste your system instructions, user queries, or document passages into the editor.
- Inspect Token Counts: Review token counts broken down by model: GPT-4o (cl100k/o200k), Claude 3.5, and Gemini.
- Evaluate Estimated Cost: Inspect the calculated price per call based on published provider token rates.
- Refine & Optimize: Trim verbose words or repetitive whitespace to lower token consumption before sending API calls.
Input & Output Example
Input:
You are an expert full-stack engineer. Please review the following code and output suggestions.
Output:
Character Count: 92 | Word Count: 14
GPT-4o Tokens: ~17 tokens (Est. Cost: $0.00004)
Claude 3.5 Sonnet: ~16 tokens
Gemini 1.5 Pro: ~16 tokens
Frequently Asked Questions (FAQ)
Q. Why do Korean and Japanese texts consume more tokens than English?
A. Standard tokenizer vocabularies (like OpenAI cl100k or o200k) are trained predominantly on English corpora. Common English words are single tokens, whereas Asian characters frequently require 2 to 4 tokens per syllable or character.
Q. Does this counter use real BPE tokenization?
A. Yes. It leverages client-side BPE tokenizer algorithms matching OpenAI tiktoken standards to deliver authentic tokenization metrics without sending your prompt to any API.
Q. What is the difference between input and output token pricing?
A. AI providers charge significantly more for output (completion) tokens than input (prompt) tokens because generation requires auto-regressive inference step-by-step.
Engineering & Privacy Notes
- Runs offline in browser memory; proprietary enterprise prompts are never logged or sent across networks.
- Pricing tables reflect public OpenAI, Anthropic, and Google official API token schedules.
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