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Token count updates live as you type// count tokens for any LLM model instantly
Count tokens in your text for GPT-4, Claude, Gemini, and other LLMs instantly. Estimate API costs and optimize your prompts with our free browser-based token counter.
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Token count updates live as you typePick the LLM you're targeting — GPT-4o, Claude, Gemini, Llama, and more are supported.
Drop in your prompt, document, or any text content. Tokens update live as you type.
See context window usage, estimated API cost per call, and full text stats at a glance.
Tokens are the basic units LLMs use to process text — roughly 3–4 characters or ¾ of a word in English. This tool estimates token counts using model-specific approximations and shows context window usage and API cost for each major LLM provider.
A token is the smallest unit of text an LLM processes. In English, one token is roughly 3–4 characters or about ¾ of a word. Common words like "the" are one token; longer or rarer words may split into multiple tokens. Numbers, punctuation, and whitespace also count as tokens.
This tool uses approximation algorithms based on each model's known tokenizer behavior. For GPT models, we approximate the cl100k_base (tiktoken) behavior. For Claude, we mimic Anthropic's tokenizer. The counts are very close but may differ slightly from the exact tokenizer output.
Cost is estimated using publicly listed per-1M-token pricing from each provider. Input tokens and output tokens have separate rates — output tokens typically cost more. The "estimated total" assumes an output of roughly equal length to the input. Always check the provider's pricing page for the latest rates.
The context window is the maximum number of tokens a model can process in a single request, including both the prompt (input) and the generated response (output). If your input plus expected output exceeds the context window, the request will fail or be truncated.
Currently supported: GPT-4o (128k context), GPT-4 (8k), GPT-3.5 Turbo (16k), Claude 3 (200k), Claude 3.5 Sonnet (200k), Gemini 1.5 Pro (1M), Gemini 2.0 Flash (1M), and Llama 3 70B (128k). More models will be added over time.
No. All token counting happens entirely in your browser using JavaScript. Your text is never sent to any server, stored, or logged. This tool is 100% client-side and private.
Each model uses a different tokenizer (vocabulary of subword units). GPT models use tiktoken with cl100k_base; Claude uses Anthropic's own tokenizer; Gemini uses SentencePiece. The same text may tokenize differently across these vocabularies, leading to slightly different counts.
Remove unnecessary filler words, avoid repeating context already established, use concise phrasing, and break large tasks into smaller sequential prompts. For documents, consider chunking or summarizing before including in a prompt. Our Prompt Optimizer tool can help with this automatically.
A token counter is a tool that estimates how many tokens a piece of text will consume when sent to a large language model (LLM) like GPT-4, Claude, or Gemini. Understanding token counts is critical for developers, prompt engineers, and anyone using AI APIs at scale — because API pricing, context window limits, and model behavior all depend on tokens, not characters or words.
JLV DevTools' Token Counter gives you an instant, live estimate of token usage across eight popular LLM models, along with API cost estimates, context window visualization, and basic text statistics — all running entirely in your browser with no data sent to any server.
Modern LLMs don't process text character by character or word by word. Instead, they use a technique called tokenization — splitting text into chunks called tokens using a learned vocabulary of subword units. In English, a single token corresponds to roughly 3–4 characters, or about ¾ of an average word.
Some examples:
"the" → 1 token"developer" → 2–3 tokens depending on the model12345 → 1–3 tokensNon-English languages often require more tokens per word — Chinese, Japanese, Arabic, and other scripts can be 2–4× less token-efficient than English, which significantly affects API costs for multilingual applications.
There are three main reasons to track token usage carefully:
1. Context window limits. Every LLM has a maximum number of tokens it can process in a single request, called its context window. If your prompt plus the expected response exceeds that limit, the API will return an error or truncate the result. GPT-4o supports 128,000 tokens; Claude 3 supports up to 200,000; Gemini 1.5 Pro supports a remarkable 1,000,000. Knowing your token count lets you stay safely within these limits.
2. API cost control. LLM providers charge per token — typically with different rates for input (prompt) tokens and output (completion) tokens. Output tokens are usually more expensive. For production applications making thousands of API calls per day, a 20% reduction in token usage can translate to significant monthly savings. This tool gives you a cost estimate based on current published pricing so you can project expenses before running large batches.
3. Model performance. Very long prompts can degrade model attention, especially for models with smaller effective context windows. Keeping prompts concise and well-structured often produces better, more focused responses. Token counters help you trim unnecessary content before sending.
Each major LLM family uses its own tokenizer, which means the same text will have different token counts depending on the model:
tiktoken library with the cl100k_base vocabulary. Well-documented and open source.countTokens method for exact counts.In practice, token counts for the same English text vary by only 5–15% across these models. Non-English and code-heavy content can show larger differences.
If you're hitting context limits or want to lower API costs, here are practical strategies:
These terms are often used interchangeably but have a subtle distinction. The context window is the total tokens a model can "see" at once — including the system prompt, conversation history, user message, and generated response. The max tokens or completion limit is specifically the cap on output length.
When you send a 10,000-token prompt to GPT-4o (128k context), you have up to 118,000 tokens available for the response. But if you request a 200,000-token output, the model will stop at its output limit regardless. Always account for both input and expected output when planning token budgets.
API pricing changes regularly, but as of early 2026, approximate rates per million tokens are:
Always verify current pricing with the provider directly — rates change frequently and vary by tier, volume, and region.