AI Token Counter & LLM Tokenizer Guide (2026): BPE, Context Windows & API Cost Math

How Byte Pair Encoding (BPE) Tokenization Works

Large language models do not read raw characters or space-delimited words. Instead, they process sequences of integer token IDs produced by a subword tokenizer—most commonly Byte-Level Byte Pair Encoding (BPE). Byte-level BPE starts with a base vocabulary of 256 individual UTF-8 bytes and iteratively merges the most frequent adjacent byte pairs across a massive training corpus until reaching a target vocabulary size (such as 100,256 or 200,019 tokens).

Before merging bytes, modern tokenizers apply a pre-tokenization regular expression that splits input text into words, numbers, punctuation runs, and leading spaces. In tiktoken, a leading space is attached to the following word—meaning " token" (with a leading space) is a single token, while "token" at the start of a line is a different token ID.

You can inspect live token counts, character ratios, and per-model API costs directly in your browser using BytePane's AI Token Counter—all calculations run locally with zero server uploads.

cl100k_base vs o200k_base vs Llama 3 & Claude 3.5

Not all LLM tokenizers segment text identically. Vocabulary size and pre-tokenization regex rules determine how efficiently a model compresses English prose, source code, numbers, and non-English scripts:

Token-to-Word Ratios by Content Type

The widely cited rule of thumb—1 token ≈ 0.75 words or 1 token ≈ 4 characters—only holds for clean English prose. When estimating context usage for RAG pipelines, tool-calling schemas, or code assistants, content type shifts token density dramatically:

Before sending large JSON payloads in system prompts or tool responses, run them through our JSON Minifier and verify the reduction in the AI Token Counter.

Context Windows, Output Caps & API Cost Math

A model's context window (e.g., 128,000 tokens on GPT-4o, 200,000 tokens on Claude 3.5 Sonnet, or 1M–2M tokens on Gemini 1.5 Pro) is a shared budget across four components:

• System Prompt & Tool Definitions: Every JSON Schema function definition injected into the request consumes input tokens on every turn.
• Conversation History & RAG Context: Retrieved document chunks and prior assistant messages accumulate linearly across multi-turn sessions.
• Reasoning / Thinking Tokens: On reasoning models (o1, o3-mini, Claude 3.7 extended thinking), internal chain-of-thought tokens are billed as output tokens even when hidden from the final text response.
• Visible Output Tokens: Capped by max_output_tokens (typically 4,096 to 16,384 tokens on standard chat models, up to 64K–100K on reasoning models).

interface TokenUsage {
  uncachedInputTokens: number
  cachedInputTokens: number
  outputTokens: number
}

interface ModelPricingPerMillion {
  input: number       // e.g. $2.50 / 1M for GPT-4o
  cachedInput: number // e.g. $1.25 / 1M for GPT-4o cached prefix
  output: number      // e.g. $10.00 / 1M for GPT-4o output
}

export function calculateRequestCostUSD(usage: TokenUsage, pricing: ModelPricingPerMillion): number {
  const inputCost = (usage.uncachedInputTokens / 1_000_000) * pricing.input
  const cachedCost = (usage.cachedInputTokens / 1_000_000) * pricing.cachedInput
  const outputCost = (usage.outputTokens / 1_000_000) * pricing.output
  return Number((inputCost + cachedCost + outputCost).toFixed(6))
}

Frequently Asked Questions