Claude Sonnet 4.6 Token Counter
Estimate token counts for Claude Sonnet 4.6 — Anthropic's balanced model. 200K context window, ~3.5 chars/token average.
Text in, token estimate out
A rough rule: ~4 characters per token for GPT/Llama-style tokenizers, ~3.5 for Claude. Whitespace and punctuation are usually their own tokens. Code, URLs, and non-English text tokenize less efficiently.
The quick brown fox jumps over the lazy dog.
~11 tokens (GPT-4 tokenizer: 10) ~13 tokens (Claude tokenizer: 12) 0.005% of a 200K context window
What you'll use this for
A token counter is a cheap pre-flight check — it tells you if your prompt fits, how much context you have left for output, and what each call will roughly cost.
Pre-flight checks
Verify a prompt fits before sending — catch over-limit inputs before you waste a call.
Cost forecasting
Pair with the cost calculator to estimate spend per message at any volume.
Prompt iteration
See how edits affect token count — trim system prompts, compact examples.
Context budgeting
Plan how much context to leave for output. Reasoning models eat tokens fast.
How to estimate tokens
Paste your text
Drop your prompt, document, or sample into the left editor. Unicode is fine — it's counted as bytes via UTF-8.
Pick a model
Each model has its own tokenizer heuristic and context window. Switch to compare.
Read the count
The right panel shows token estimate, context fill bar, characters, words, and remaining headroom.
Compare with sibling tools
Jump to a cost calculator to translate tokens into dollars, or compare another model.
Frequently asked questions
The estimate uses ~4 characters per token (~3.5 for Claude). Real tokenizers may differ by ±10–20% depending on language and content. For exact counts use the provider's official tokenizer.
Claude's tokenizer is slightly different and tends to produce more tokens for the same text. The 3.5 ratio is a rough average that gives closer results than 4 across typical English prose.
Yes. No signup, no limits, no ads. Runs entirely in your browser.
No. The estimate covers content tokens only. System prompts, tool definitions, and chat scaffolding add additional tokens that this tool does not see.
This tool gives a count, not a tokenization view. For Anthropic's exact view use the Anthropic Console or their tiktoken-like tools. OpenAI publishes tiktoken for the same purpose.
About LLM tokenization
Modern language models don't see characters or words — they see tokens, sub-word units produced by a byte-pair encoding (BPE) tokenizer trained on the model's corpus. The same text produces different counts in different tokenizers because each tokenizer learned a different vocabulary.
Why estimates vary
- Language — English tokenizes most efficiently (~4 chars/token). Non-Latin scripts often need 2–4× more tokens per character.
- Code & URLs — long identifiers and URL slugs tokenize poorly. Symbols and whitespace also count.
- Tokenizer drift — Claude, GPT, Gemini, and Llama all use different vocabularies. The 4-chars/token rule is a first-order approximation, not a guarantee.
When exact counts matter
- Near the limit — if you're within 5% of the context window, use the model's official tokenizer (Anthropic Console,
tiktoken, etc.). - Cost-sensitive batches — at scale a 10% over-estimate compounds; the official tokenizer is the source of truth for billing.
- Streaming budgets — output tokens count too. Reserve enough headroom for the model's reply.
For day-to-day pre-flight checks — "does this prompt fit?" — the heuristic this tool uses is plenty.