TechCompare LogoTechCompare
Back

LLM Token Counter

Count the tokens in any text or prompt for GPT-5, GPT-4, Llama, Claude, and Gemini. Exact text tokenization for supported OpenAI and Llama 3 models, runs entirely in your browser, nothing is uploaded.

…tokens

Loading the exact o200k_base tokenizer in your browser…

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

How to use this tool

  1. Paste or type your text

    Type a prompt, paste an API request body, or drop in any text you plan to send to a language model. The counter updates the count immediately as you type, no submit button required.

  2. Choose the target model

    Select the target model. Supported OpenAI and Llama 3 encodings count plain text tokens. Llama 4, Claude, and Gemini show labeled estimates. Verify production counts with the model tokenizer or provider API.

  3. Read the token count

    The tool returns the token count alongside word, character, sentence, and line counts so you can compare all the metrics at once. Use the token number to check against the model's context window or price per million tokens.

  4. Trim the prompt if needed

    If the count exceeds your budget or context window, edit the text in place and the count drops live. Common wins include dropping redundant instructions, merging examples, or summarising prior messages that no longer need full context.

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.

Supported OpenAI encodings and the Llama 3 tokenizer count text locally. Llama 4 uses a different tokenizer and is shown as an estimate here, alongside Claude and Gemini. Text counts exclude chat templates, system formatting, and multimodal inputs. Check provider usage for billing.

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 can be words, word fragments, punctuation, or byte sequences. The selected tokenizer determines the count. This tool loads supported OpenAI and Llama 3 encodings for text counts, while Llama 4, Claude, and Gemini are estimated.

Exact vs estimated counts

The exact label means the selected text tokenizer loaded successfully, not that the number equals a full API bill. Chat templates and other inputs add tokens. Llama 4, Claude, Gemini, and tokenizer-load fallbacks show estimates. Use provider usage or the model tokenizer for production verification.

Popular token guides

Practical explainers on tokenization, context windows, and cost optimization for LLM applications.

What are tokens
The 4-chars-per-token rule and how LLMs see text
View details ➜
OpenAI vs Llama tokenizer
Why the same text costs different tokens per model
View details ➜
Token limits by model
Context windows from 128K to Llama 4 Scout's 10M tokens
View details ➜

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?
The supported OpenAI encodings and Llama 3 tokenizer count plain text tokens exactly when loaded successfully, excluding chat-template and API overhead. Llama 4, Claude, and Gemini use rough estimates with no fixed accuracy guarantee. Llama 4 has a different tokenizer, so the Llama 3 count must not be presented as exact for it.
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?
The tokenization and counting run locally in your browser, and the tool does not upload the text for counting. The current text is also synchronized into the page URL, so do not paste secrets into a URL that may be copied, stored in history, synced, or shared.

Related tools

LLM API Pricing Calculator

Compare API costs across major models (OpenAI, Anthropic, Google) with prompt caching.

Use tool ➜

LLM VRAM Calculator

Calculate the VRAM needed to run or fine-tune any LLM at any quantization.

Use tool ➜

Text Encoding Converter

Convert between Text, Base64, Binary, Hexadecimal, and Decimal formats.

Use tool ➜

JSON Formatter

Validate, format, and minify JSON data with readable output and error detection.

Use tool ➜

Use this tool on your own site

Drop the Token Counter into your blog, docs, or CMS with one iframe snippet. Free, no signup. See all embeddable tools