Count AI tokens before they become API spend.

The AI Token Cost Calculator is the cost-focused part of Free Token Counter. Paste a prompt, choose a model, set an expected output size and monthly run count, and the page estimates tokens, input cost, output cost, total request cost, monthly spend, context usage, and prompt efficiency before you call an AI API.

Tokens are the small text units that AI models read and generate. A token can be a word, part of a word, punctuation, whitespace, or a code fragment. Tokenization varies by model, so the same prompt may have different token counts across OpenAI, Claude, Gemini, and other LLM providers.

Token costs determine how much an AI request will cost and how much of the model context window your prompt consumes. Calculating token costs helps you compare prompt versions, reduce repeated text, budget high-volume workflows, and avoid sending oversized prompts to production models.

Yes. The Compare tab shows the same prompt across the models included in the calculator, including OpenAI, Anthropic Claude, Google Gemini, DeepSeek, Mistral, Grok, Llama, and Qwen options. OpenAI tokenization is exact for supported encodings, while other providers are clearly labeled as estimates.

Yes. The model list and pricing table are designed to be updated as providers release new models or change token pricing. The current pricing data in this calculator is marked as checked on 2026-05-31, and displayed costs should be treated as estimates.

Among the paid models currently included in this calculator, GPT-5.5 Pro has the highest displayed output price at $180 per 1M output tokens. This is based on the calculator data checked 2026-05-31, not a guarantee that it is the most expensive AI model globally.

Among paid API models currently shown, DeepSeek V4 Flash has the lowest combined displayed input and output price at about $0.42 per 1M input tokens plus 1M output tokens. Self-hosted models such as Llama are listed separately because their cost depends on infrastructure rather than provider token pricing.

AI tokens are calculated by a tokenizer, which splits text into model-specific pieces. English text often averages around four characters per token, but code, JSON, symbols, whitespace, and non-English text can change the ratio. This tool uses exact OpenAI tokenization where supported and estimates other providers.

In generative AI, a token is one unit of text the model processes when reading a prompt or producing an answer. Models predict and generate text token by token, so token count affects cost, speed, context usage, and the amount of information that can fit in a request.

As a rough English-language estimate, 200,000 tokens is about 150,000 words, or roughly 300 pages at 500 words per page. The exact amount depends on language, formatting, code, JSON, and the tokenizer used by the selected model.

AI providers usually price tokens per 1 million input tokens and per 1 million output tokens. Input tokens are the prompt and context you send; output tokens are the model response. Total request cost is estimated by applying each rate to the matching token count.

A common estimate is that 1,000 tokens equals about 750 English words. That is around 1.5 pages at 500 words per page, or roughly 2 to 3 double-spaced pages depending on formatting, headings, code, and language.

One AI token is usually a small piece of text, not a fixed dollar amount. The price of one token depends on the provider and model. For example, a model priced at $1 per 1M input tokens costs $0.000001 per input token before output tokens are counted.

1M tokens means one million AI tokens, which is the standard billing unit many AI providers use. In rough English text, 1 million tokens is about 750,000 words, but the exact word count changes with language, formatting, code, and tokenizer behavior.