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LLM Token Counter

Count the tokens in any text or prompt for GPT-5, GPT-4, Llama, Claude, and Gemini. Exact tokenisation for OpenAI and Llama models, runs entirely in your browser, nothing is uploaded.

0tokens

Exact count for GPT-5 / GPT-4o using the o200k_base tokenizer.

17
Words
88
Characters
72
Characters (no spaces)
2
Sentences
1
Lines
0
Tokens / word

About this tool

The LLM Token Counter tells you how many tokens a piece of text will use before you send it to a model. Tokens are the units language models actually bill and budget by, so knowing the count up front helps you stay inside context windows, control API costs, and trim prompts that are larger than they need to be. Paste any prompt, choose a model, and you get the token count alongside word, character, sentence, and line statistics.

For OpenAI and Llama models the count is exact, computed with the same Byte Pair Encoding tokenizers those models use (o200k_base for GPT-5 and GPT-4o, cl100k_base for GPT-4 and GPT-3.5, and the Llama 3 tokenizer for Llama 3 and 4). For Claude and Gemini the tool shows a clearly labelled estimate, because those tokenizers are not openly available to run client-side. Everything happens locally in your browser, so your text is never uploaded.

Why token count matters

Every model has a context window measured in tokens, and most APIs bill per input and output token. Counting tokens before a request lets you confirm a prompt fits, predict cost, and decide whether to summarise or chunk long inputs. It is especially important for retrieval-augmented generation and agentic loops, where appended context can quietly push a request over the limit.

Tokens vs words vs characters

Tokens do not map cleanly to words. Common words are often a single token, while rare words, code, emoji, and non-English text can split into several tokens each. As a rough guide, English text averages about four characters or three-quarters of a word per token, but the only reliable number is an actual tokenizer count, which is what this tool provides for OpenAI and Llama models.

Exact vs estimated counts

OpenAI and Meta publish their tokenizers, so GPT and Llama counts here are exact. Anthropic and Google do not ship browser-runnable tokenizers, so their counts are approximated from text length and marked as estimates. Use the estimate to plan, but treat the exact numbers as authoritative when you need a precise budget.

Frequently asked questions

What is a token in an LLM?
A token is a chunk of text that a language model reads as a single unit. It is usually a common word, part of a longer word, or a piece of punctuation rather than a whole word or a single character. As a rough rule of thumb, one token is about four characters of English text, and 100 tokens is roughly 75 words.
How accurate is this token counter?
For OpenAI and Llama models the count is exact, because it uses the same Byte Pair Encoding tokenizers those models ship (o200k_base for GPT-5 and GPT-4o, cl100k_base for GPT-4 and GPT-3.5, and the Llama 3 tokenizer for Llama 3 and 4). For Claude and Gemini the count is a labelled estimate, since those tokenizers are not publicly available to run in the browser. Estimates are typically within about 10% of the real value.
Why do different models report different token counts?
Each model family is trained with its own tokenizer and vocabulary, so the same sentence can split into a different number of tokens depending on the model. Newer vocabularies like OpenAI's o200k_base are generally more efficient, packing more characters into each token, which lowers the count compared to older tokenizers.
Is my text sent to a server?
No. All tokenization and counting happens locally in your browser using a tokenizer that loads on the page. Nothing you type or paste is uploaded, logged, or stored, which makes the tool safe to use for private prompts and confidential text.

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