Prompt Token Estimator

A prompt token estimator is a tool that measures the size of a text prompt and estimates how many tokens it will consume when sent to a large language model (LLM). Tokens are the units that models process — roughly 3.7–4 characters per token for English text, but varying by language, content type, and model family. Knowing your token count before sending helps you stay within a model's context window, budget your output tokens, and avoid truncation errors.

No — they are estimates. This tool uses documented approximation formulas (for example, characters ÷ 3.9 for GPT-4o / GPT-4 / GPT-3.5, or characters ÷ 3.7 for Claude). Actual token counts depend on the model's specific tokenizer vocabulary. The tool clearly labels each result as "~approx" and shows the method and review date next to every estimate. Characters, words, lines, paragraphs, and UTF-8 bytes are exact local measurements.

For typical English prose and code, the estimates are accurate to within ±5–10%. Accuracy decreases for non-Latin text (Chinese, Arabic, Japanese, Korean), heavily formatted content, emoji, and specialized vocabularies. The tool shows a diagnostic notice when it detects a high byte-to-char ratio or emoji, which are signals that actual token usage may be higher than estimated.

Different model families use different tokenizer vocabularies. OpenAI GPT-4o, GPT-4, and GPT-3.5 use the cl100k_base vocabulary (100K tokens). Claude uses Anthropic's proprietary BPE tokenizer. Gemini uses SentencePiece. LLaMA-3 uses a custom 128K BPE vocabulary. Each vocabulary assigns different token boundaries to the same input text, so identical prompts can produce different token counts on different models. The profile selector lets you choose the closest approximation to your target model.

A context window (also called a context limit or context length) is the maximum number of tokens an LLM can process in a single request — including both the input prompt and the output response. For example, GPT-4o has a 128K token context window. If your prompt plus the expected output exceeds this limit, the model will truncate content or refuse the request. The headroom meter in this tool shows what fraction of the selected context window your prompt currently occupies.

Reserved output tokens is the number of tokens you set aside for the model's response within the context window. If you want a 1,000-token response from a model with a 128K window, your prompt can use at most 127,000 tokens. The tool subtracts your reserved output budget from the context window to show net input headroom and warns when your prompt plus reserved output exceeds the window.

The headroom meter is a color-coded progress bar that shows what percentage of the selected context window your estimated prompt tokens occupy, with the reserved output tokens added on top. Blue means comfortable (under 85%); amber means caution (85–99%); red means over limit (100%+). The meter is paired with exact labels and percentage text so color is never the only signal — it also works for users who cannot distinguish colors.

The tool includes five estimation profiles: GPT-4o / GPT-4 / GPT-3.5 (cl100k approximate, chars ÷ 3.9); Claude 3.5 / Claude 3 (approximate, chars ÷ 3.7); Gemini 1.5 / 2.0 (approximate, chars ÷ 4.0); LLaMA-3 / Mistral (approximate, chars ÷ 3.8); and a generic word-based fallback (words × 1.33). Context window presets include 17 models from GPT-3.5-Turbo (16K) to Gemini 1.5 Pro (1M tokens).

No. Every calculation runs entirely in your browser using vanilla JavaScript. No text is sent to any server, API, or analytics service. Open your browser's DevTools Network tab while typing — you will see zero outbound requests. The privacy notice on the tool reads: "Measurements run locally in this browser. Token counts and costs are estimates unless a local verified tokenizer is selected."

Yes. Select the matching tokenizer profile from the dropdown: GPT-4o/GPT-4/GPT-3.5 for ChatGPT and the OpenAI API, Claude 3.5/Claude 3 for Anthropic's models, and Gemini 1.5/2.0 for Google's models. Then select the target model from the context window preset dropdown to see how much headroom your prompt leaves. The estimates are approximate but accurate enough for budgeting and planning purposes.

Words are counted by splitting the text on whitespace sequences (spaces, tabs, and newlines) and filtering empty tokens. This matches the convention used across other ToolsSonic text tools and is consistent with how Microsoft Word, Google Docs, and most writing tools count words. Hyphenated compounds like "state-of-the-art" count as one word.

UTF-8 bytes is the number of bytes required to encode your text in UTF-8, the standard encoding used by web APIs. ASCII characters use 1 byte each; most Latin-extended characters use 2 bytes; CJK characters use 3 bytes; emoji and some rare Unicode characters use 4 bytes. API providers that charge per request body size (such as for document uploads) use byte counts. Most LLM APIs charge per token, not per byte, but the byte count helps you understand how Unicode-heavy your prompt is.

When you enable "Section breakdown" in the options bar, the tool splits your prompt into prose paragraphs and fenced code blocks and shows the estimated token count for each section. This helps you identify which parts of your prompt are the most token-intensive — useful when you need to reduce size. Code blocks are identified by triple-backtick or triple-tilde fences; everything else is split by blank lines into prose paragraphs.

Emoji typically cost 2–4 tokens each in most BPE tokenizers because they are encoded as multi-byte UTF-8 sequences that do not appear in the tokenizer vocabulary as single units. The chars-per-token estimate does not capture this accurately. The tool shows a diagnostic notice when it detects emoji in your prompt, warning that actual token counts may be higher than estimated.

Non-Latin text tokenizes very differently from English. CJK characters (Chinese, Japanese, Korean) typically use 1–2 tokens per character in cl100k, not the 0.25 tokens per character implied by the English ratio. Arabic and Indic scripts also tokenize differently. The tool detects a high byte-to-char ratio and shows a diagnostic warning when your text contains many multi-byte characters, indicating that the estimate may under-count. For non-Latin text, the generic word-based profile or a model-specific API call will give better accuracy.

Pressing Ctrl+Enter (or Cmd+Enter on Mac) while focus is in the prompt input textarea triggers the Analyze action — equivalent to clicking the Analyze button. This lets you measure your prompt without moving your hands to the mouse.

Yes. Click "Open file…" to select a local .txt, .md, .prompt, .json, .html, or .log file. The file is read by the browser FileReader API entirely in memory and its text is loaded into the input. No data is sent to any server.

The session history records the last 8 Analyze operations in the current browser tab. Each entry shows the time, character count, estimated token count, profile used, and context window size. History is stored only in the JavaScript state for the current page load — it is cleared when you close or refresh the tab, and nothing is written to localStorage, sessionStorage, or cookies.

"Copy report" copies a plain-text summary to your clipboard including exact measurements (characters, words, lines, paragraphs, bytes), the token estimate with method and date, and the context headroom calculation. "↓ Download" saves the same report as token-estimate-report.txt. Neither action includes your prompt text in the filename or in any metadata. The report does contain the measurement results but not the prompt input itself.

The amber "~Approximate" badge and the description below the options bar tell you exactly which formula is being used, which model family it targets, when the profile was last reviewed, and what its limitations are. This information is always visible — never hidden behind a tooltip or modal — because the spec for this tool requires that approximate measurements be visually distinct from exact ones. If you need exact token counts, use the official tokenizer tool for your target model (such as the OpenAI Tokenizer playground for GPT models).

No. This tool focuses on a single prompt: measuring its size, estimating its token usage, and checking whether it fits within a chosen context window. It does not model multi-request workloads, API pricing, batch jobs, or total cost of ownership. A separate cost calculator tool is better suited for workload-level spend modeling. This tool intentionally does not embed or display API prices to avoid showing stale numbers.

The headroom meter turns red, the status pill shows "Over limit", and a warning banner appears with the exact token overage. The warning suggests shortening the prompt or switching to a model with a larger context window. Common strategies include removing few-shot examples, shortening system instructions, summarizing retrieved context chunks, or splitting the task into multiple smaller requests.

Yes. Paste the complete text that will make up your full API request context — system prompt, few-shot examples, retrieved documents, and the user message — all in the input area. The tool will measure the combined size. This gives you the total input token estimate for the full context window slot, which is what the model receives and what you will be charged for.