Claude Token Counter
Estimate tokens before sending your prompt to Claude Opus, Claude Sonnet, or Claude Haiku. Built around Claude’s 200K-token context window — the right tool when you’re pasting long documents, transcripts, or codebases.
Keyword density
Top 8Add a prompt to see your top keywords.
Claude’s tokenizer differs from ChatGPT’s
Claude uses Anthropic’s own tokenizer, not OpenAI’s cl100k_base. For English text the densities are similar (within ~10%), but Claude tends to be slightly more efficient on code and certain multilingual content.
This tool returns a calibrated estimate that works well as a sizing guide for any modern Claude model. For billing-grade accuracy use Anthropic’s count_tokens endpoint or the official SDK.
Claude context windows
| Model | Context window | Best for |
|---|---|---|
| Claude Haiku | 200K tokens | Fast, cheap, simple tasks |
| Claude Sonnet | 200K tokens | Balanced — most workflows |
| Claude Opus | 200K tokens | Heavy reasoning, long writing |
A 200K-token window sounds huge, but it includes the system prompt, full message history, and the model’s reply. For long-document work, leave at least 20K tokens headroom for the response.
When the context-window size starts to matter
Common Claude workflows that benefit from the 200K window:
- Analyzing PDFs, research papers, or contracts (often 30K–80K tokens)
- Reading whole codebases for refactor recommendations
- Working with meeting transcripts or interview recordings
- Long-form essay drafting where prior drafts need to stay in context
For each of these, knowing how close you are to the window limit is what keeps you out of truncated-output land. This counter shows that ratio in real time.