LLM BPE Tokenizer & Multi-Model Token Counter

PROMPT & CODE TOKENIZER INPUT

Select LLM Tokenizer Encoding

Prompt Text or Code Payload

TOKEN COUNT & METRIC ANALYSIS

Total Tokens

278

Characters

Words

Token Efficiency Breakdown

Chars per Token Ratio: 4.34 chars/token

Context Window Usage (128k): 0.05% of 128k

Estimated Input Cost (GPT-4o): $0.00016 USD

Byte-Pair Encoding (BPE) & LLM Tokenization Guide

Tokenization is the foundational process by which Large Language Models convert raw text strings into arrays of numerical token IDs. Understanding tokenization helps developers optimize prompt costs and avoid context window overflows.

Tokenization Benchmarks across Models

English Prose: ~1 token ≈ 4 characters or 0.75 words.
Source Code (JS/Python): ~1 token ≈ 2.5 characters (due to indentation and punctuation).
JSON Payloads: Heavy token overhead from curly braces, quotes, and key repetitions.

Related AI Developer Tools

Calculate multi-model API costs with our AI API Pricing Calculator, test template variables using the Prompt Variable Tester, or build system prompts with the AI Prompt Optimizer.

Frequently Asked Questions

Common questions about this tool.

What is a Byte-Pair Encoding (BPE) tokenizer? ▼

BPE tokenization splits text into subword units (tokens) based on frequency statistics. A single token typically represents ~4 characters or ~0.75 words in English text.

Why do different LLM models have different token counts? ▼

Each model family (OpenAI cl100k_base / o200k_base vs Anthropic vs Llama 3 128k vocabulary) uses a custom vocabulary dictionary. Code snippets and non-English text vary significantly across tokenizers.

How do code and special characters affect token count? ▼

Indentation spaces, punctuation, JSON brackets, and unicode characters require dedicated tokens, making code files ~30% denser in tokens than plain prose.

What is the maximum context window for GPT-4o and Claude 3.5 Sonnet? ▼

GPT-4o supports a 128,000 token context window, while Claude 3.5 Sonnet supports a 200,000 token context window.

How can I optimize token consumption to reduce API costs? ▼

Remove redundant whitespace, minify JSON payloads, replace verbose instructions with concise system rules, and use prompt caching for static contexts.