LLM Token Counter
Paste text, see exact token counts and side-by-side cost projections for GPT-5, Claude 4, Gemini 2.0, Llama 3.1, and Mistral — 16 models in one view. Estimate per-call, per-day, and per-month spend before you ship.
An LLM token counter is a tool that measures how many tokens a piece of text will use when sent to a large language model. Tokens are the units LLMs charge per, so counting them is how developers predict API cost and plan prompts. This counter runs client-side and covers 16 models across OpenAI, Anthropic, Google, Meta, and Mistral, with side-by-side per-request and per-month cost projections.
| Model | Tokens | Context used | Per call | Per day | Per month |
|---|---|---|---|---|---|
GPT-5 OpenAI | 0 | 0.00% | $0.007500 | $7.50 | $225.00 |
GPT-5 mini OpenAI | 0 | 0.00% | $0.000600 | $0.6000 | $18.00 |
GPT-4o OpenAI | 0 | 0.00% | $0.005000 | $5.00 | $150.00 |
GPT-4o mini OpenAI | 0 | 0.00% | $0.000300 | $0.3000 | $9.00 |
GPT-4 Turbo OpenAI | 0 | 0.00% | $0.0150 | $15.00 | $450.00 |
GPT-3.5 Turbo OpenAI | 0 | 0.00% | $0.000750 | $0.7500 | $22.50 |
Claude 4 Opus Anthropic· estimated | 0 | 0.00% | $0.0375 | $37.50 | $1,125.00 |
Claude 4 Sonnet Anthropic· estimated | 0 | 0.00% | $0.007500 | $7.50 | $225.00 |
Frequently asked questions
What is an LLM token?
A token is the smallest unit of text an LLM processes. It is roughly a syllable or short word — English averages about 4 characters per token, but code and non-Latin scripts tokenize differently. LLM APIs charge per token, so counting tokens is how you predict cost.
How accurate is this counter?
Very accurate for OpenAI models (GPT-3.5, GPT-4, GPT-4o, GPT-5) — we use the official tiktoken tokenizer running client-side in JavaScript, giving exact counts. Claude, Gemini, Llama, and Mistral counts are estimates because their tokenizers are not published as portable JavaScript libraries. Estimates are within about 10 percent of actual for typical English text.
Does my text get sent to a server?
No. The tokenizer runs entirely in your browser using pure JavaScript. Your prompt, code, or document never leaves the page. You can verify this in the network tab.
Why does the same text produce different token counts for GPT-4 vs Claude?
Each model family trains its own tokenizer on its own corpus. GPT uses byte-pair encoding (BPE) with either cl100k or o200k vocabularies. Claude uses a different BPE. Gemini and Llama use SentencePiece. Different vocabularies split the same string into different token boundaries — so a 100-word English paragraph might be 120 tokens on GPT-4o and 130 tokens on Claude 4 Sonnet.
How can I reduce token usage without hurting quality?
Four patterns work: (1) trim the system prompt of anything the model already knows, (2) use JSON instead of natural language for structured instructions, (3) move examples into a fine-tuned model instead of a few-shot prompt, (4) use a smaller model (GPT-4o mini, Claude Haiku, Gemini Flash) for classification and routing tasks — reserve the flagship for reasoning steps.
Are the prices up to date?
Prices reflect public API pricing as of mid-2026 and are labeled per 1 million tokens. LLM providers change prices frequently, so treat the cost figures as a planning estimate. For exact billing use the provider dashboard.
What is the difference between input and output tokens?
Input tokens are what you send to the model (system prompt + user message + any RAG context). Output tokens are what the model generates back. Output tokens are almost always more expensive than input — often 3 to 5 times as much. When designing prompts, minimizing output is usually the biggest cost lever.
How does this help me govern my AI systems?
Counting tokens is step one. Step two is registering each production model, tracking the prompts and versions that ship to users, and flagging when a prompt drift blows up your token budget. Zilonex Govern is the governance platform that does this end-to-end — this tool is the free planning aid that shows why governance matters.