Prompt Length / Token Counter
Count characters, words, and estimated tokens in your prompt live, and check it against common context window limits.
Type or paste your prompt and watch character, word, sentence, and estimated token counts update live, then check the total against a rough context-window limit so you know how much room you have left before you hit an error mid-request.
How to use it
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Paste your prompt
Type or paste it into the box — stats update as you go.
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Check the counts
Characters, words, sentences, and estimated tokens update live.
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Compare to a limit
Pick a context window from the dropdown, or enter your own, to see how much room is left.
Why it helps
Token counts matter more than character or word counts once you’re working with long prompts, few-shot examples, or system prompts close to a model’s limit. AI models don’t process text as words — they break it into tokens, chunks that are usually a bit shorter than a full word for common English text, but can be longer or shorter depending on punctuation, whitespace, and language. That mismatch between what you see (words) and what the model counts (tokens) is exactly why prompts that look short on screen can quietly eat through more of a context window than expected.
This tool uses the common ≈4-characters-per-token approximation, a rule of thumb that’s close enough for most English prose to flag when you’re approaching a limit, even though it won’t match any specific model’s exact tokenizer. Code, non-English text, and unusual formatting all tend to tokenize less predictably than plain prose, so treat the estimate as a warning system, not a precise measurement.
Why token counts matter more than they used to
Early chatbot interfaces made context limits mostly invisible — a short conversation rarely got close to any ceiling. That’s changed. Long system prompts, multi-shot examples, pasted documents, and multi-turn agent workflows all consume context fast, and running out mid-task usually means truncated instructions or a dropped conversation history, not just a clean error message. Knowing your rough token count before you hit send is the difference between debugging a mysterious quality drop and understanding exactly why it happened.
This matters even more for anything meant to be reused — a system prompt embedded in an app, a template shared across a team, or a prompt library entry — since those get pasted into different contexts with different available headroom every time they’re used.
Reading the context-window options
The dropdown groups context windows into rounded classes — short-context assistant, mid-range assistant, Claude Sonnet/Opus class, and GPT-5-class or Gemini Pro-class — rather than pinning to one exact model version. That’s deliberate: context window sizes change often as providers release new model versions, sometimes within weeks of each other, so a number hardcoded to one specific model name goes stale fast. If you’re checking against a specific deployment, use the Custom limit option and enter your provider’s documented figure directly rather than relying on the presets.
Who this is for
Anyone building prompts that push toward a model’s limits benefits from this — developers assembling system prompts with several few-shot examples, teams maintaining a shared prompt library where prompts get pasted into different tools with different limits, or anyone who’s had a long prompt silently underperform and wants to rule out length as the cause before troubleshooting anything else. It’s also worth a quick check before pasting a long document into a chat window — knowing roughly how much of your budget the document alone will consume tells you upfront how much room is left for instructions and follow-up questions in the same conversation.
Tips for better results
- Treat the token count as a ceiling check, not a target. A shorter prompt isn’t automatically a better one.
- Recount after big edits. Adding a few examples can add more tokens than it looks like on screen.
- Use the custom limit for system prompts. If you’re building a reusable system prompt, checking it against your actual deployment’s limit matters more than the general presets.
FAQ
Is the token count exact?
No. It’s an estimate based on the common ≈4-characters-per-token rule of thumb. Actual tokenization varies by model and can differ, especially for code, non-English text, or unusual formatting.
Why do the context window options say “class” instead of a specific model name?
Context window sizes change often between model versions. These are rounded reference points — check your provider’s current documentation for the exact number, especially if you’re working close to a limit.
Does this tool send my prompt anywhere?
No. Everything runs in your browser; nothing is uploaded or stored.
Why does my prompt use more tokens than I’d expect from the word count?
Punctuation, unusual formatting, code, and non-English text all tend to tokenize less efficiently than plain English prose, so the token count can run noticeably higher than a simple word count would suggest.
Should I always try to minimize token count?
No — the goal is staying under your limit with room to spare, not writing the shortest possible prompt. Cutting context or examples to save tokens often hurts output quality more than the extra length was costing you.