AI Token Counter
Estimate how many tokens your prompt uses and what it will cost across GPT-5.5, GPT-4.1, Claude Opus 4.8, Gemini 3.1, and more. Paste your text for a live character / word / token count, add your expected output tokens and requests-per-day, and get per-model input cost, output cost, per-call total, and a daily projection — with context-limit warnings when your usage runs long. Fast in-browser estimate (±15%), no signup, nothing uploaded.
How to Use This Tool
- Paste your prompt text. The character, word, and estimated token counts update live as you type — or click Load Sample to see it in action.
- Set expected output tokens. Enter roughly how many tokens you expect the model to generate (a paragraph is ~100, a long answer ~500–1,000) so output cost can be priced.
- Set requests per day to turn a per-call cost into a daily projection for planning at volume.
- Compare the model cards. Each shows input cost, output cost, the per-call total, and the daily total — side by side across twelve models from OpenAI, Anthropic, Google, xAI, Mistral, and open-model hosts.
- Watch the context warnings. A card flags when your input plus expected output approaches 80% of, or exceeds, that model's context window.
- Copy the stats as a plain-text summary to drop into a doc, ticket, or budget.
About Tokens, Pricing & Why It's an Estimate
If you build anything on top of large language models, two numbers govern your life: how many tokens your text uses, and what those tokens cost. Tokens are the units a model reads and writes — not quite words, not quite characters, but common chunks of text that a tokenizer carves your input into. Every API call is billed by tokens, both the ones you send (input) and the ones the model generates (output), and every model has a context window measured in tokens that caps how much it can handle at once. Get a feel for token counts and you can predict cost, avoid context-limit errors, and choose the right model for the job. That's what this tool is for: paste text, see the estimated tokens, and watch the cost land across twelve current models.
The single most useful fact about tokens is the rough conversion: for English, one token is about four characters or roughly three-quarters of a word, so 1,000 words is on the order of 1,300 tokens. It's only an approximation because tokenizers behave differently on different content — common words collapse to a single token while rare words, names, numbers, code, and non-English text get split into several. This counter takes the larger of a character-based and a word-based estimate, so dense low-space text like code isn't under-counted, but the honest framing is that any in-browser estimate sits within about ±15% of the provider's exact count. For budgeting, model comparison, and catching a prompt that won't fit, that precision is plenty; for reconciling an invoice to the token, use each provider's official tokenizer or usage dashboard.
Cost has structure worth understanding. Providers price input and output tokens separately, and output is almost always far more expensive — often three to four times the input rate — because generating text costs more than reading it. That changes how you optimize: a verbose system prompt is cheap compared to letting the model ramble in its answer, so capping output length is frequently the biggest lever on your bill. This tool reflects that by pricing your input tokens and your expected output tokens at each model's distinct rates, then summing them into a per-call total and multiplying by your requests-per-day for a projection. Seeing GPT-5.5 next to Claude Sonnet 4.6 next to Gemini Flash-Lite at your actual volume often reveals an order-of-magnitude cost difference for work any of them could do.
The context window is the other constraint the cards surface. It's the combined budget for your input and the model's output; exceed it and the call fails or silently drops content. Windows range widely — 128K on Llama 3.3 70B, 200K on Claude Haiku 4.5, 256K on Mistral Medium 3.5, and around 1M on GPT-5.5, Claude Opus 4.8, Gemini 3.1, and Grok 4.3 — and bigger isn't automatically better, because filling a huge window costs more and can slow responses. When your estimated input plus expected output crosses 80% of a model's window, this tool warns you; when it would exceed the window, it flags that in red, so you catch the problem here instead of in a failed production request.
Estimating tokens is step one; systematically cutting LLM spend is the bigger prize. Our AI-Powered Marketing team helps brands reduce LLM costs by 40–70% through prompt optimization, model routing (sending easy work to cheaper models), and output caching — without sacrificing quality. Pair this counter with the AI Prompt Builder to write tighter prompts that use fewer tokens, the Readability Checker to grade the output, and the Keyword Density Checker for deeper text statistics.
Frequently Asked Questions
What is a token in AI models?
A token is the basic unit of text that a large language model reads and generates. It is not quite a word and not quite a character — tokenizers split text into common chunks, so a token might be a whole short word, part of a longer word, a punctuation mark, or a space. For English, a useful rule of thumb is that one token is roughly four characters or about three-quarters of a word, so 100 tokens is about 75 words. Models bill by tokens (both the tokens you send and the tokens they generate) and their context limits are measured in tokens, which is why estimating token counts matters for both cost and whether your prompt will fit. This tool gives a fast token estimate for any text and turns it into per-model cost figures.
How many tokens are in a word?
On average, English text runs about 1.3 tokens per word, or equivalently around 0.75 words per token — so 1,000 words is very roughly 1,300 tokens. But it varies: common words often map to a single token, while rare words, names, code, numbers, and other languages get split into several tokens each, pushing the ratio higher. Whitespace and punctuation also consume tokens. Because of this variability, any word-to-token conversion is an approximation. This tool takes the larger of a character-based estimate (about four characters per token) and a word-based estimate (about 0.75 words per token) — the more conservative figure, so dense text like code isn't under-counted — but you should still treat the result as a ballpark within roughly ±15% of what the real tokenizer would report.
How does the GPT tokenizer (tiktoken) work?
OpenAI's models use a byte-pair-encoding tokenizer (the open-source library is called tiktoken). BPE starts from individual bytes and merges the most frequently occurring pairs into larger tokens, building a vocabulary of common subword chunks. The result is that frequent words become single tokens while uncommon words are broken into familiar pieces — for example a rare technical term might split into three or four tokens. Different model families use different vocabularies (newer GPT-5 and GPT-4.1 models use different encodings than older GPT-3.5), so the exact count for the same text can differ slightly between models. For an exact count you would run the text through tiktoken itself; this tool uses a fast heuristic instead so it can run entirely in your browser with no library download, trading a little precision for speed and privacy.
Does Claude tokenize differently from GPT?
Yes. Anthropic's Claude models use their own tokenizer, which is distinct from OpenAI's tiktoken, so the same piece of text can produce a somewhat different token count on Claude than on GPT-5 or GPT-4.1. In practice the two are usually in the same ballpark for ordinary English prose, but they can diverge more on code, unusual formatting, or non-English text. Google's Gemini uses yet another tokenizer again. Because this tool is model-agnostic — it applies one heuristic to your text — it reports the same estimated token count across every model and then varies only the pricing and context limits. That keeps it simple and fast; just remember the per-model real counts will differ by a few percent, which is well within the ±15% margin to keep in mind.
What's the difference between input and output tokens?
Input tokens are everything you send to the model — your prompt, system message, conversation history, and any documents you include. Output tokens are everything the model generates in its response. This distinction matters because providers almost always charge a higher rate for output tokens than for input tokens (often three to four times more), since generating text is more expensive than reading it. That means a short prompt that produces a long answer can cost more than a long prompt with a short answer. This tool estimates your input tokens from the text you paste, and lets you enter the number of output tokens you expect, then prices each side separately at each model's input and output rate so the per-call total reflects the real billing structure.
What is a context window?
The context window is the maximum number of tokens a model can consider at once — it includes both your input and the model's output. If the combined total exceeds the window, the request fails or older content gets truncated. Context windows have grown enormously: older models like GPT-3.5 were 4K to 16K tokens, but today's flagships such as GPT-5.5, GPT-4.1, Claude Opus 4.8, Gemini 3.1 Pro, and Grok 4.3 run to around 1M tokens, while Claude Haiku 4.5 is 200K. A bigger window lets you feed in long documents, large codebases, or lengthy conversations, but using more of it costs more (you pay per token) and can slow responses. This tool shows each model's context limit and warns you when your input plus expected output is approaching or exceeding it, so you catch a too-long request before you send it.
What are the token limits for each model?
Approximate context limits for the models in this tool: GPT-5.4 mini is around 400K tokens, GPT-5.5 and GPT-4.1 are about 1M, Claude Opus 4.8 and Sonnet 4.6 are 1M (Haiku 4.5 is 200K), Gemini 3.1 and Grok 4.3 are about 1M, Mistral Medium 3.5 is 256K, and Llama 3.3 70B is 128K. These are the total budget shared between your prompt and the response, not separate allowances. Providers update these periodically and offer variants with different limits, so treat the numbers here as current-generation guidance rather than guarantees, and check the provider's documentation for the exact model version you are calling. The tool flags when your estimated usage crosses 80% of a model's window and again when it would exceed it.
Why is this an estimate (±15%)?
Exact token counts require running your text through each provider's specific tokenizer, which means downloading a large vocabulary file or calling an API — neither of which fits a fast, private, in-browser tool. Instead, this counter uses a heuristic that takes the larger of a characters-per-token and a words-per-token estimate, which is accurate enough for planning but not exact. Real counts drift from the estimate depending on how much code, punctuation, rare words, numbers, or non-English text your input contains, and they differ slightly between OpenAI, Anthropic, and Google tokenizers. As a result the figure here is typically within about ±15% of the true count. For budgeting, comparing models, and catching context-limit problems that margin is fine; for exact billing reconciliation, use each provider's official tokenizer or usage dashboard.