LLM Token Counter

How to Use LLM Token Counter

The LLM Token Counter estimates how many tokens a prompt, document, code block, JSON payload, or multilingual passage will consume across popular model families. Paste your text into the textarea and the result table updates with token counts for Gemini 3.5 Flash, Gemini 3.1 Pro, Claude Opus 4.7, Claude Sonnet 4.6, GPT-5, and GPT-5 mini. Each row also shows the percentage of the model context window consumed by the text and the estimated input API cost.

The example buttons are useful when you want to understand typical workloads. The Wikipedia-style sample resembles an article with paragraphs and entities. The code sample contains punctuation-heavy syntax that usually tokenizes differently from prose. The PDF/RAG sample imitates extracted document text that might be chunked, embedded, retrieved, and then placed into a model prompt.

Use the budget area for reverse calculation. Enter a budget, choose a currency, and set a typical number of tokens per request. The calculator estimates how many input tokens that budget can buy and how many requests it can cover for each model. This is useful when planning batch summarization, prompt evaluation, data labeling, or RAG backfill jobs.

Formula & Theory - LLM Token Counter

The LLM Token Counter uses practical browser-side token estimation. Exact tokenization depends on the provider’s tokenizer, model version, normalization rules, and special tokens. For planning and comparison, the calculator uses a weighted approximation:

English token estimate ≈ English characters / 4
Chinese/Japanese/Korean estimate ≈ CJK characters × 1.5
Code adjustment ≈ punctuation and syntax markers × 0.35
Context usage percentage = token count / model context window × 100%

Input cost is estimated with the same per-million-token pricing convention used by API providers:

Input API cost =
  estimated tokens / 1,000,000 × model input price

The GPT rows use a GPT-style approximation designed to behave similarly to common OpenAI tokenizers such as cl100k_base and o200k_base. Claude rows add a small family multiplier to reflect different segmentation. Gemini rows use a SentencePiece-style approximation where a token is roughly four characters or about three quarters of a word. These rules are intentionally transparent so the estimate remains fast, local, and easy to reason about.

For very large inputs, the calculator can process the text asynchronously in a Web Worker. That keeps the page responsive when users paste long reports, exported PDF text, or files above 100K characters. The calculation still happens entirely in the browser.

Use Cases for LLM Token Counter

The LLM Token Counter is useful whenever token size affects price, latency, or model fit. Prompt engineers can compare prompt variants before production deployment. Developers can estimate whether a JSON payload, code file, or retrieved context block will fit inside GPT-5, Claude, or Gemini. Content teams can estimate summarization cost before processing a long article archive.

For RAG systems, the calculator helps decide how much retrieved context to include. Too little context may reduce answer quality, while too much context increases latency and cost. For code assistants, it helps estimate how many files can be included in one request. For multilingual products, it makes visible the fact that Chinese, Japanese, and Korean text may not map to tokens the same way English prose does.

The internal links connect this page to the LLM API Cost Calculator and the AI Model Context Window Comparator. Together they form a workflow: count tokens, check whether they fit, then estimate daily and monthly cost.