Live in-browser tokenizer · priced across 12 models
Paste a prompt or document to count its tokens live, then see what it costs as input on every major model — and whether it even fits each model's context window.
| Model | As input | Fits context? |
|---|---|---|
| GPT-4o mini OpenAI | $0.00000 | ✓ |
| DeepSeek-V3 DeepSeek | $0.00001 | ✓ |
| Gemini 2.5 Flash Google | $0.00001 | ✓ |
| Mistral Large Mistral | $0.00002 | ✓ |
| Llama 3.3 70B (Groq) Meta / Groq | $0.00002 | ✓ |
| Claude Haiku 4.5 Anthropic | $0.00003 | ✓ |
| GPT-5 OpenAI | $0.00004 | ✓ |
| Gemini 2.5 Pro Google | $0.00004 | ✓ |
| o3 OpenAI | $0.00007 | ✓ |
| GPT-4o OpenAI | $0.00008 | ✓ |
| Claude Sonnet 4.5 Anthropic | $0.00010 | ✓ |
| Claude Opus 4.5 Anthropic | $0.00016 | ✓ |
Token counts use OpenAI's o200k encoding (exact for GPT-4o/GPT-5; a close approximation for Claude and Gemini). Prices per 1M tokens, USD, LiteLLM table (updated 2026-05-22).
Drop in a prompt, system instruction, or whole document. Tokenizing happens in your browser as you type — nothing is sent anywhere.
Tokens, words, and characters update instantly. Tokens are what you actually pay for, and they rarely match word count — punctuation, code, and rare words cost more.
The table prices your text as input on each model and flags any model whose context window it would overflow.
Counts use OpenAI's o200k tokenizer — exact for GPT-4o/GPT-5, and a close approximation for Claude and Gemini, which use their own tokenizers.
A free tool gives you a hypothesis. The 30-minute diagnostic is where we pressure-test it against your actual workflows — and decide whether the project is worth building, buying, or skipping.