Code Flux

Token Counter

Estimate how many tokens your text contains for LLM API usage and cost calculation.

0
Estimated Tokens
$0.000000
Input Cost
$0.000000
Output Cost (same length)

Text Statistics

Characters:0
Words:0
Lines:1
Chars/Token:0

* Token counts are estimates based on common tokenization patterns. Actual counts may vary depending on the specific tokenizer used by each model. Pricing shown is per 1K tokens based on publicly available rates.

Deep-Dive Technical Documentation

Byte Pair Encoding (BPE): The Algorithm Behind Modern Tokenizers

Almost every major LLM — GPT-4, Claude, Llama, Mistral — uses a variant of Byte Pair Encoding for tokenization. BPE was originally a data compression algorithm proposed by Philip Gage in 1994, and was adapted for NLP by Sennrich et al. in 2016. The training process starts with a vocabulary of individual bytes (or characters) and iteratively merges the most frequent adjacent pair into a single new token. For example, if 'th' appears more often than any other bigram in the training corpus, it becomes a single token. Then the algorithm rescans and might merge 'th' + 'e' into 'the'. This continues for tens of thousands of iterations until the vocabulary reaches a target size — typically 50,000 to 100,000 tokens for production models. The result is a vocabulary that captures common words as single tokens ('the', 'function', 'import') while still being able to represent any arbitrary string by falling back to character-level or byte-level tokens. This is why rare words, variable names, and non-English text consume more tokens per character than common English words.

Context Windows: Architecture Limits and Practical Constraints

Every transformer-based LLM has a fixed context window — the maximum number of tokens it can process in a single forward pass. GPT-4 Turbo supports 128K tokens, Claude 3 supports 200K, and Gemini 1.5 Pro claims up to 1M. But the context window is not free storage: attention computation scales quadratically with sequence length in standard transformers (O(n²) for self-attention), which means doubling the context length quadruples the memory and compute required. Techniques like FlashAttention, sliding window attention (used in Mistral), and ring attention help manage this, but the practical effect for developers is that stuffing the full context window degrades both latency and output quality. Models tend to recall information at the beginning and end of the context much better than content in the middle — a phenomenon researchers call the 'lost in the middle' problem. Token Counter helps you stay aware of how much of the context budget your prompt consumes so you can make intentional trade-offs between context size and response quality.

Token-to-Cost Mapping: How API Billing Actually Works

LLM API pricing is always quoted per 1,000 or per 1 million tokens, but the billing mechanics have nuances that catch people off guard. First, input and output tokens are priced differently — output tokens are typically 2–5× more expensive because generation requires sequential autoregressive decoding (each token depends on all previous tokens), while input processing can be parallelized. Second, system prompts count as input tokens on every single API call, so a 500-token system prompt on an endpoint handling 10,000 requests per day silently adds 5 million input tokens to your monthly bill. Third, few-shot examples in the prompt are re-tokenized and re-billed with every request — they are not cached across calls (unless you use prompt caching features like those offered by Anthropic). Token Counter's cost estimation multiplies your token count against the selected model's per-token rates so you can forecast expenses before committing to a prompt design.

Estimation vs. Exact Count: Why Client-Side Approximation Works

Token Counter uses a heuristic estimation rather than running an actual tokenizer like tiktoken (OpenAI's Python library) or the SentencePiece model files used by Llama and Mistral. The heuristic is based on well-documented ratios: English text averages roughly 1.3 tokens per word and 4 characters per token; code tends to run higher at 1.5–2.0 tokens per word due to variable names, operators, and punctuation being split into separate tokens; and CJK characters (Chinese, Japanese, Korean) typically map to 1 token per 1–2 characters because they are less represented in BPE training data. The estimation accuracy for English prose is typically within 5–10% of the exact count, which is more than sufficient for budgeting and prompt design decisions. Running an exact tokenizer in the browser would require loading multi-megabyte vocabulary files and executing thousands of merge operations — feasible but heavyweight for a quick estimation tool. The trade-off is intentional: fast, private, and accurate enough to be useful without downloading model-specific assets.

What is Token Counter?

If you're shipping anything on top of GPT-4, Claude, Gemini, or an open-weight model like LLaMA — you need to know how many tokens your prompts burn before you hit 'send.' Token Counter gives you that number in real time, right in your browser, with cost projections across the major models. Here's the thing about tokens: they're not words. LLMs use subword tokenization (usually Byte-Pair Encoding) that splits text into statistically-derived chunks. Common English words like 'the' or 'hello' map to a single token. Longer or rarer words get chopped up — 'tokenization' becomes something like 'token' + 'ization'. Punctuation, brackets, and whitespace each eat their own tokens too. For typical English prose, expect about 1 token per 4 characters or ~1.3 tokens per word. Code is way worse — all those braces, semicolons, and camelCase names push the ratio up 2–3x. CJK characters can cost 2–3 tokens each. This matters for two reasons: money and limits. LLM APIs bill per token, separately for input and output, with output tokens costing 2–5x more than input. And every model has a hard context window — 128K tokens for GPT-4 Turbo, 200K for Claude 3 Opus, just 16K for GPT-3.5 Turbo. Go over and the API either rejects your request or silently chops off the beginning of your prompt. This tool lets you paste your prompt, see the estimated token count update instantly, and compare what that same text would cost across GPT-4, GPT-3.5, Claude Opus, Sonnet, and Haiku — so you can pick the right model for the job. Everything runs client-side. Your prompts never leave your machine.

How to Use

  1. Paste your prompt, system message, or any text into the input area. The tool accepts everything from single-line queries to multi-thousand-word documents including code snippets, JSON payloads, and markdown.
  2. Watch the token count, character count, word count, and characters-per-token ratio update instantly in real-time as you type or edit — no need to click a button.
  3. Select a specific LLM model from the dropdown (GPT-4, GPT-4 Turbo, GPT-3.5 Turbo, Claude 3 Opus, Sonnet, or Haiku) to see the estimated cost per 1K tokens for both input and output.
  4. Review the cost cards to compare how much your text would cost to send across different models, helping you decide which model offers the best balance of capability and price.
  5. Use the Text Statistics bar to monitor your characters-per-token ratio — a ratio significantly below 4.0 may indicate your text is heavy on special characters or code syntax, which consume more tokens per character.

Common Use Cases

  • Pre-flight cost estimation for production API calls: paste your prompt template and input data to project monthly costs before launching an AI feature.
  • Context window budgeting: verify that your system prompt + user message + expected output fits within the model's token limit (128K for GPT-4 Turbo, 200K for Claude 3 Opus) to avoid silent truncation.
  • Prompt optimization: iteratively trim your prompt while monitoring token count to find the shortest version that still produces quality output, reducing per-request costs.
  • Model comparison: paste the same prompt and compare cost projections across GPT-4, GPT-3.5 Turbo, Claude Opus, Sonnet, and Haiku to choose the most cost-effective model for your use case.
  • RAG pipeline tuning: estimate how many document chunks fit within the context window alongside your system prompt and user query in Retrieval-Augmented Generation architectures.
  • AI product planning: forecast API costs for different user volumes by estimating average tokens per request and multiplying by expected daily/monthly call counts.
  • Fine-tuning dataset preparation: estimate the total token count of training data to predict training costs and time for fine-tuning jobs on OpenAI or Anthropic platforms.

Frequently Asked Questions

Most LLMs use Byte-Pair Encoding (BPE). It starts with individual characters, then iteratively merges the most common adjacent pairs into new tokens until the vocabulary hits a target size (GPT-4's is ~100K). The result: frequent words like 'the' and 'function' get their own token, while rare words get split — 'unforgettable' becomes something like 'un' + 'forget' + 'table'. It's a sweet spot between a manageable vocabulary and keeping sequences short enough for the attention mechanism to handle.
It uses a heuristic based on word count, special character density, and the ~4 characters-per-token baseline. For normal English prose, you'll get within 90–95% of the real BPE count. For code, JSON, or non-Latin scripts (CJK, Arabic), it can drift further because those inputs have very different token densities. If you need exact counts, you'd run tiktoken or the model's own tokenizer. But for quick 'will this fit in the context window?' checks, this gets you there without sending your text to anyone's server.
Input tokens are what you send (your prompt and context). Output tokens are what the model generates back. Output costs 2–5x more because generation is computationally brutal — the model runs its full autoregressive decoding loop for every single output token, while input gets processed in one forward pass. GPT-4 Turbo, for example: $0.01 per 1K input tokens but $0.03 per 1K output. That difference adds up fast at scale.
It's the max number of tokens (input + output combined) a model can handle in one request. GPT-4 Turbo gives you 128K (~300 pages of text), Claude 3 Opus goes up to 200K, GPT-3.5 Turbo caps at 16K. Blow past the limit and the API either errors out or silently drops the oldest tokens from your prompt — which is a nasty surprise if you're doing RAG or multi-turn chat and suddenly your context disappears.
Code is packed with special characters — brackets, semicolons, operators, underscores — and each one tends to be its own token. Add in camelCase variable names and technical terms that the BPE training corpus didn't see often, and a line of Python can easily tokenize to 2–3x more tokens than an English sentence of the same length. JSON is especially bad: all those quotation marks, colons, commas, and braces around every key-value pair eat tokens like crazy.
A few moves that make a real difference: (1) Trim your system prompt ruthlessly — it ships with every single request. (2) Use JSON mode or function calling to constrain output and avoid the model rambling. (3) Summarize older conversation turns instead of sending the full chat history. (4) Pick the right model for the job — GPT-3.5 Turbo and Claude Haiku are 10–60x cheaper and handle classification, extraction, and simple generation just fine. (5) Cache responses for repeated queries so you're not paying for the same answer twice.

Client-Side Sandbox Security Verification

Zero server transmission. All processing runs entirely within your browser's JavaScript sandbox using native browser-compiled APIs. 0% of your data payloads ever cross an external server boundary, origin log, or third-party endpoint.

Browser-native compilation. Operations like JSON.parse(), btoa()/atob(), encodeURIComponent(), and the Intl API are executed by the browser engine itself (V8, SpiderMonkey, or JavaScriptCore) — no WebAssembly payloads, no remote execution, no server-side eval.

Independently verifiable. Open your browser's DevTools > Network tab while using any tool. You will see zero outbound requests containing your data. This is a verifiable, auditable privacy architecture.