AI Token Counter
Count tokens for GPT-5.4, Claude, Llama 4, and Gemini models. See estimated API costs and compare token counts across LLMs side by side.
About AI Token Counter
Understanding how LLMs break your text into tokens is key to managing API costs and staying within context limits. This AI token counter runs tokenization locally in your browser and shows exact or estimated counts for the most popular models.
How it works
Paste any text into the editor and the tool instantly counts tokens using real tokenizer libraries. GPT-4o uses the o200k_base encoding, while GPT-4 and GPT-3.5 Turbo use cl100k_base. For Claude and Llama, the tool applies cl100k_base as a close approximation and marks those results as estimates.
Cost comparison at a glance
The comparison panel shows token counts and estimated input costs for every model side by side. This makes it easy to see, for example, that a 2,000-token prompt costs fractions of a cent on GPT-3.5 Turbo but several cents on GPT-4. If your prompts are long, pair this with the word counter to track length as you write.
Tokens per word ratio
The tokens-per-word metric helps you build intuition for how verbose a model's tokenizer is on your specific content. English prose typically lands around 1.3 tokens per word, while code, URLs, or non-Latin scripts can push higher. Use the character counter alongside this tool if you also need byte-level stats.
Everything runs client-side, so your text stays private and never touches a server.
Frequently Asked Questions
What is a token in the context of LLMs?
A token is a chunk of text that a language model processes as a single unit. Tokens can be whole words, parts of words, or even individual characters. On average, one token is roughly 0.75 words in English, but this varies by language and content type.
Are the Claude and Llama token counts exact?
The counts for Claude and Llama are estimates. This tool uses OpenAI's cl100k_base tokenizer as a proxy, which is a close approximation. The actual token count may differ by a small percentage depending on the model's specific tokenizer.
Why do different models produce different token counts?
Each model family uses its own tokenizer with a different vocabulary. GPT-4o uses the o200k_base tokenizer with a 200,000-token vocabulary, while GPT-4 and GPT-3.5 use cl100k_base with 100,000 tokens. Larger vocabularies tend to produce fewer tokens for the same text.
Does this tool send my text to any server?
No. All tokenization happens entirely in your browser using the gpt-tokenizer library. Your text never leaves your device.
How are the API costs calculated?
Costs are based on publicly listed input token pricing for each model. The tool multiplies your token count by the per-token rate. Output tokens, which models charge separately, are not included in this estimate.