LLM Token Counter & API Cost Calculator
Estimate Byte-Pair Encoded (BPE) tokens and calculate live API execution costs across OpenAI, Claude, Gemini, and DeepSeek.
Prompt Canvas & Parameters
Token Metrics & Cost Matrix
0
Tokens
0
Characters
0
Words
0.0
Chars/Token
| Model | Tier | Input / 1M | Est. Cost (1x) |
|---|
How Tokenization Works in Modern LLMs
Large Language Models do not read text character-by-character or word-by-word. Instead, text is mapped into numerical fragments called tokens using algorithms such as Byte-Pair Encoding (BPE). Common English words often constitute single tokens, while rare words, code formatting, multi-byte non-Latin scripts, and emojis are divided into multiple subword tokens.
5 Proven Strategies to Cut Token Overhead
- Trim Redundant Whitespace: Excessive indentation and double-spacing add unnecessary token fees.
- Minify Injected JSON: Strip whitespace and indentation from API context arrays before appending to system prompts.
- Eliminate Markdown Fluff: Replace verbose explanations with concise bullet points in few-shot examples.
- Use Shorter Delimiters: Instead of
<system_context_instructions_payload>, use compact XML tags like<context>. - Leverage Prompt Caching: For high-frequency prompts, models like DeepSeek-V3 and Claude offer 80–90% cost discounts on cached input tokens.