How many words is 1,000 tokens?

For standard English text, 1,000 tokens is approximately 750 words. The accepted benchmark across OpenAI (tiktoken) and Anthropic models is 1 token ≈ 0.75 words.

Why are non-English tokens and words different?

Tokenizers are predominantly optimized for English corpora. In non-Latin or morphologically rich languages like Greek, Arabic, or Chinese, a single word can split into 3 to 5 tokens, making API calls significantly more expensive.

How do code and JSON tokenize compared to plain text?

Structured code and JSON contain indentation spaces, syntax symbols, brackets, and camelCase identifiers that tokenizers fragment aggressively. For code, 1 token typically equals roughly 0.3 words.

Tokens to Words Converter: AI Tokenizer Ratios

Translate LLM Tokens into readable English words (and vice versa) for OpenAI GPT-4o, Claude 3.5, and DeepSeek. Features real-time text analysis and API input cost estimates.

💡 Related Calculators: ChatGPT API Cost Calculator • Prompt Caching Savings Optimizer • LLM VRAM Calculator

2026 LLM Tokenization Ratios & Benchmarks

Empirical token-to-word conversion metrics across standard BPE tokenizers (OpenAI tiktoken, Anthropic, and DeepSeek).

Data / Language Syntax	Words per 1,000 Tokens	Tokens per Word	Input Cost (DeepSeek-V3)	Input Cost (GPT-4o)
Standard English Prose	~750 words	~1.33 tokens	$0.00014	$0.0025
Code, JSON & YAML Payloads	~300 words	~3.33 tokens	$0.00014	$0.0025
Western European (Spanish, French, German)	~450 words	~2.20 tokens	$0.00014	$0.0025
Greek, Russian & Cyrillic Alphabets	~250 words	~4.00 tokens	$0.00014	$0.0025
CJK (Chinese, Japanese, Korean)	~180 words	~5.50 tokens	$0.00014	$0.0025

Frequently Asked Questions

Why does the 1 token = 0.75 words ratio fail for non-English languages?

Byte-Pair Encoding (BPE) tokenizers are trained on predominantly English corpora. Non-Latin scripts (such as Greek, Cyrillic, or Arabic) cannot map whole words to single vocabulary tokens, fragmenting into multiple byte-level tokens per word and increasing processing costs by 2x to 4x.

Why do codebases and JSON payloads consume so many tokens?

Code syntax relies heavily on whitespace indentations, brackets ({, }, [, ]), semicolons, and camelCase or snake_case identifiers. Tokenizers split these into discrete punctuation tokens, averaging roughly 3.3 tokens per word of source code.

How do tokens convert to standard book or document pages?

A single-spaced formatted page contains roughly 500 English words (~667 tokens). A double-spaced academic manuscript page averages 250 words (~333 tokens). Therefore, a 100k token context window accommodates approximately 150 single-spaced pages.