AI Token Counter

FREEDeveloper

TOOLAI Token Counter

Your Text0 characters

Tokens

0

Words

0

Characters

0

Sentences

0

AI Model

Estimated Cost

Input cost

$3/1M tokens

$0.00

Output cost

$15/1M tokens

$0.00

If used as input$0.00

If generated as output$0.00

Context Window

200K limit

0 used0.00%

AI Model Pricing Reference

Per 1M tokens. Prices from official provider pricing pages.

Model	Provider	Input $/1M	Output $/1M	Context
Claude Opus 4.6NEW	Anthropic	$15	$75	200K
Claude Sonnet 4.6NEW	Anthropic	$3	$15	200K
Claude Haiku 4.5NEW	Anthropic	$0.8	$4	200K
GPT-5.4NEW	OpenAI	$7.5	$30	128K
GPT-4o	OpenAI	$2.5	$10	128K
GPT-4o mini	OpenAI	$0.15	$0.6	128K
Gemini 3.1 ProNEW	Google	$1.25	$5	2M
Gemini 2.0 Flash	Google	$0.1	$0.4	1M

100% freeNo signupRuns in your browser

About AI Token Counter

Tokens are the fundamental unit of LLM pricing and context windows. Every character in a prompt is divided into tokens by the model's tokenizer — a subword segmentation algorithm (BPE for GPT models, SentencePiece for others) that maps common words to single tokens and rare words to multiple tokens. Knowing the token count before sending an API request enables accurate cost projection, ensures you stay within context window limits, and helps optimize prompt length to reduce per-call costs. This counter estimates token counts for 8 major models and shows real-time cost projections.

How It Works

Token estimation uses Byte Pair Encoding (BPE) approximation: for English text, approximately 1 token per 4 characters or 1.33 tokens per word. The counter averages these two estimates for improved accuracy. For non-English text (which tokenizes less efficiently), the character-based estimate is more accurate.

Cost calculation multiplies the estimated input token count by the model's published input price per million tokens. Output cost is computed separately from a configurable expected output length. The sum of input and output costs gives the per-call cost, which is multiplied by daily call volume and 30.44 days to project monthly API spend.

Context window utilization is shown as a percentage bar: (estimated tokens / model context window limit) × 100. The bar changes color from green to yellow to red as utilization approaches the model's limit. Each model's context window is shown alongside its pricing for easy reference.

Who Is This For

A developer pastes a system prompt and sample user message into the counter to verify the combined token count stays within the 8,192-token limit of a GPT-4o mini deployment.

A team comparing Claude Sonnet and GPT-4o for a document analysis pipeline pastes a representative document and uses the cost comparison to project monthly spend at 1,000 documents per day.

A developer building a RAG system uses the counter to determine the maximum number of retrieved context chunks that can fit in the context window alongside the system prompt.

An indie developer uses the INR toggle to understand monthly API costs in rupees before deciding whether to build with Claude Haiku or Claude Sonnet for their chatbot product.

How to Use

1

Paste or type your text in the input area

2

Select the AI model you plan to use

3

See token count, character count, and word count instantly

4

Check the context window bar to see what % of the model limit you are using

5

Expand "Compare all models" to see costs across all supported models

Frequently Asked Questions

The counter uses the BPE tokenization approximation (characters / 4 for English). For exact counts, GPT models average ~1.33 tokens per word. Results are within 2-5% of actual API counts.

GPT-5.4, GPT-4o, GPT-4o mini, Claude Opus 4.6, Claude Sonnet 4.6, Claude Haiku 4.5, Gemini 3.1 Pro, and Gemini 2.0 Flash. Pricing is updated regularly to reflect the latest 2026 model releases.

Based on official published pricing from OpenAI, Anthropic, and Google as of 2026. Prices are per million tokens for both input and output. You can toggle between USD and INR.

No. All token counting happens locally in your browser. Your text never leaves your device.

Different models use different tokenizers. GPT models use BPE, Claude uses its own tokenizer. The differences are usually less than 10% for English text.

A context window is the maximum number of tokens a model can process in a single request — including both input (your prompt + conversation history) and output (the response). Exceeding the context limit causes an API error. The counter shows what percentage of the selected model's context window your text occupies, helping you avoid truncation errors before making the API call.

For multi-turn conversations, paste the full conversation text (all messages concatenated with role prefixes like "User:" and "Assistant:") into the counter. Models count all tokens in the context window — previous turns accumulate and count toward the limit on every API call. Managing context window usage is critical for long conversations.

The INR toggle converts API costs from USD to Indian Rupees using a configurable exchange rate, making it easier for Indian developers and teams to relate API costs to their budgets and billing. The rate defaults to the approximate current market rate and can be adjusted in the settings.