Estimate token counts for different LLM models
Token counts are estimates based on average character-to-token ratios. Actual counts vary based on vocabulary, language, and special characters. For precise counts, use the official tokenizer (tiktoken for OpenAI, etc.).
Tokens are the fundamental units that large language models process — they are subword pieces rather than whole words. A token is typically 3-4 characters in English, so 1,000 tokens is roughly 750 words. GPT-4 uses the cl100k_base tokenizer with a 128K context window. Claude uses a similar BPE tokenizer with up to 200K tokens of context. Common English words are usually one token, while uncommon words may be split into multiple tokens. Punctuation, spaces, and special characters each consume tokens. Code typically requires more tokens than prose because variable names, symbols, and formatting each count. Understanding token counts is critical for LLM API cost management (APIs charge per token), staying within context window limits, and optimizing prompts to maximize the useful content within token budgets.
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