Compare LLM Tokenizers
Compare LLM Tokenizers shows how many tokens the same text costs on ChatGPT, Claude, and Gemini, side by side — every count accurate. Paste text once, pick a model per vendor, and see the differences instantly.
Why compare tokenizers?
Each model family tokenizes text differently, so the same prompt can be, say, 100 tokens on ChatGPT, 120 on Claude, and 110 on Gemini. Because you pay per token and each model has a context limit, those differences directly affect cost and how much you can fit in a request. Comparing them for your actual text is the only way to know.
How the counts are produced
| Vendor | How it's counted | Accuracy |
|---|---|---|
| ChatGPT (OpenAI) | Public tiktoken encoding, in your browser | Exact |
| Claude (Anthropic) | Official count_tokens API | Exact |
| Gemini (Google) | Official countTokens API | Exact |
There's no shortcut for Claude and Gemini: their modern tokenizers aren't public, so an accurate count has to come from each vendor's own API.
How to compare
- Paste or type your text.
- Pick one model per vendor (an OpenAI encoding, a Claude model, a Gemini model).
- Select Compare token counts to see all three side by side, with a relative bar and characters-per-token for each.
Example
A short English paragraph often lands within a few percent across all three vendors, while code or non-English text can diverge more. The relative bars make the gap obvious at a glance.
Limitations
- A free sign-in is required (the Claude and Gemini counts use their official APIs); there is no credit cost.
- The comparison is for the specific models you select — token counts can shift between model generations.
- Need a per-token visual breakdown? Use the ChatGPT Tokenizer (OpenAI's tokenizer is open); Claude and Gemini expose counts only.
