How accurate is this counter?

These are heuristic estimates based on each family's average characters-per-token ratio, not the exact tokenizer. For English prose they are typically within 5-10% of the real count; for code or non-Latin scripts the gap can be larger.

Why do different models show different counts?

Each model family uses a different tokenizer (BPE vocabulary). GPT models use tiktoken, Claude uses its own vocabulary, and Llama/Mistral use SentencePiece, so the same text splits into different numbers of tokens.

Is my text sent anywhere?

No. Everything runs in your browser. Nothing you paste is uploaded, stored, or logged.

How do tokens relate to words?

For typical English, one token is about three-quarters of a word, so 100 tokens is roughly 75 words. Punctuation, whitespace, and rare words split into more tokens.

What is the Token Counter (Multi-model)?

Paste any text and get token estimates across multiple tokenizer families (GPT/tiktoken, Claude, Llama, Mistral) at once, with character and word counts plus quick cost math. Runs fully in your browser. It runs free in your browser on Gera Tools, with nothing uploaded.

Token Counter (Multi-model)

Name: Token Counter (Multi-model)
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

Get one useful tool a week

Like this tool? Enter your email and we'll send you one genuinely useful Gera tool a week — plus a link to come back to this one. No spam, one-click unsubscribe any time.

Multi-model token counter

Paste any text and instantly see how many tokens it is likely to use across the major model families — GPT (tiktoken), Claude, Llama, and Mistral — plus raw character and word counts. Useful for checking whether a prompt fits a context window or for estimating API cost before you send it.

How the estimate works

Exact token counts come from each model’s tokenizer, which we can’t bundle in a lightweight browser tool. Instead this counter uses each family’s measured average characters-per-token ratio and adjusts for whitespace and word boundaries. GPT models average roughly 4 characters per token for English; Claude and the SentencePiece-based Llama and Mistral families differ slightly, which is why the columns don’t match exactly. For English prose the estimates are usually within 5–10% of the real count.

What the token count is used for

Token counts drive several important decisions in LLM development and usage:

Fitting the context window. Every model has a maximum context window — the total number of input and output tokens it can process in one call. Knowing your prompt’s token count lets you check whether it fits, and by how much. A 2,000-token prompt in a 4,096-token window leaves room for about 2,000 output tokens; the same prompt in a 128,000-token window leaves almost unlimited room for context.

Estimating API cost. Most providers bill by the million tokens. Knowing the token count of your prompt multiplied by your call volume gives a direct cost projection. A 500-token prompt at 10,000 calls per day is 5 million input tokens per day.

Building RAG systems. In retrieval-augmented generation, you retrieve text chunks and insert them into the prompt. The token count of each chunk determines how many chunks fit within your budget, which directly sets retrieval depth.

Debugging truncation. If model outputs are suddenly cut short, an unexpected jump in prompt token count is the first thing to check. The counter shows you immediately whether a recent content addition pushed the prompt past a threshold.

How to interpret differences between model families

The same text producing 980 tokens on GPT and 1,050 tokens on Llama is normal and expected. Each family trained its own tokenizer on a different corpus with a different vocabulary size. Larger vocabularies tend to produce fewer tokens per word; smaller vocabularies produce more. For practical purposes, use the family-specific estimate for the model you are actually calling rather than averaging them.

Tips

Code, JSON, and non-Latin scripts tokenise less efficiently — expect more tokens than the English-tuned estimate suggests.
For an exact count before a critical, high-volume call, run the provider’s own tokenizer (tiktoken for OpenAI, Anthropic’s count-tokens endpoint for Claude).
Remember both your prompt and the model’s reply count toward the context window — leave headroom for the output.
The word and character counts displayed alongside the token estimate help you calibrate: if 1,000 words reads as 750 tokens, the estimate is running in the normal 0.7–0.8 tokens-per-word range for English.