- How many words is 1000 tokens in GPT-4 and Claude?
- Approximately 750 words for standard English text. The ratio of 1 token = 0.75 words (or ~4 characters) holds for both OpenAI's GPT-4 (tiktoken) and Anthropic's Claude tokenizers when processing English prose. Code, structured data, and non-English languages will produce different ratios — typically more tokens per word.
- Why do GPT-4 and Claude have different context window sizes?
- GPT-4 supports up to 128K tokens (~96,000 words), while Claude 3 supports up to 200K tokens (~150,000 words). Google's Gemini Pro offers up to 1M tokens (~750,000 words). These limits reflect architectural decisions about attention mechanism memory, training data, and intended use cases. Larger context windows allow processing entire books, codebases, or lengthy documents in a single request.
- How do I calculate OpenAI API costs from token counts?
- Multiply your input tokens by the per-token input price and output tokens by the per-token output price. For GPT-4 Turbo (2026 pricing): ~$0.01/1K input tokens and ~$0.03/1K output tokens. A 10,000-token prompt generating a 2,000-token response costs approximately $0.10 + $0.06 = $0.16. Use this converter to estimate token counts from your word counts, then apply your provider's current pricing.
- Why does code use more tokens than regular English text?
- Code contains special characters (brackets, semicolons, operators), camelCase identifiers, and formatting that tokenizers split into more subword units. A 100-word code snippet might use 150-200 tokens, compared to ~133 tokens for 100 words of English prose. JSON, XML, and heavily structured data also consume more tokens per information unit due to syntax overhead.
- How many tokens do non-English languages use per word?
- Non-English text typically uses 1.5-3x more tokens per word than English. Chinese and Japanese can use 2-3 tokens per character because BPE tokenizers are primarily trained on English text. Spanish and French use roughly 1.2-1.5x more tokens than English. Arabic and Hindi fall in the 1.5-2x range. Always test with your actual content for accurate estimates in multilingual applications.
- What is the difference between BPE and SentencePiece tokenization?
- BPE (Byte Pair Encoding) — used by OpenAI's tiktoken — learns merge rules from training data to combine frequent character pairs into tokens. SentencePiece — used by Google's Gemini and many open-source models — operates directly on raw text without pre-tokenization. Both produce similar token-to-word ratios for English but may differ for other languages, whitespace handling, and special characters.
- When should I use an exact tokenizer instead of this estimator?
- Use exact tokenizers (tiktoken for OpenAI, Anthropic API for Claude) when you are within 10% of context window limits, implementing production guardrails that truncate prompts at specific token counts, calculating precise API costs for billing reports, or building RAG systems where chunk size accuracy affects retrieval quality. For planning, budgeting, and prompt design iteration, this estimator provides sufficient accuracy.
