Question 1

What is an AI Token Counter, and why does token counting matter?

Accepted Answer

An AI token counter tells you exactly how many tokens your input will consume before it is sent to a large language model (LLM). Instead of estimating based on word count, it uses each model provider's official tokenizer or token counting API to determine the precise number of input tokens.

This is important because AI providers bill based on tokens—not words or characters. A prompt that appears short can tokenize very differently depending on whether it contains code, JSON, Markdown, emojis, non-English text, or images.

Accurate token counting helps you:
• Estimate API costs before making a request.
• Stay within a model's maximum context window.
• Optimize prompts for lower latency and cost.
• Compare how different AI models tokenize the same content.
• Prevent requests from failing due to context length limits.

Whether you're working with plain text, PDFs, source code, or multimodal inputs, knowing the exact token count helps you build more reliable AI applications.

Question 2

How does this OpenAI Token Counter calculate GPT-5.6 and GPT-4o tokens?

Accepted Answer

Unlike many online token counters that rely on approximations or reverse-engineered tokenizers, our OpenAI token counter uses OpenAI's official APIs to calculate token usage whenever possible.

That means the reported token count matches what OpenAI itself will use when processing your request. There are no assumptions or word-to-token conversion formulas involved.

This provides accurate token counts for supported OpenAI models, including:
• GPT-5.6
• GPT-4o
• GPT-4o Mini
• Other compatible OpenAI models

Using the official tokenizer ensures consistent results across prompts containing natural language, source code, structured JSON, Markdown, function calls, Unicode characters, and other complex inputs.

Question 3

How does the Claude Token Counter calculate tokens for Anthropic models?

Accepted Answer

Our Claude token counter uses Anthropic's official count_tokens API endpoint to determine exactly how many tokens your input will consume.

Instead of trying to replicate Anthropic's tokenizer locally, your prompt is sent to Anthropic's dedicated token counting endpoint, which returns the exact token count that Claude will use when processing the request.

This supports popular Claude models such as:
• Claude Opus 4.8
• Claude 3.5 Sonnet
• Claude 3 Opus
• Other supported Anthropic models

Because the count comes directly from Anthropic, the results remain accurate even for long documents, code, PDFs, multilingual text, and other complex inputs.

Question 4

How does the Gemini Token Counter calculate Google Gemini tokens?

Accepted Answer

The Gemini token counter communicates directly with Google's official Generative Language API using the countTokens endpoint.

Google performs the tokenization on its own infrastructure and returns the exact number of input tokens for the selected model.

This provides accurate token counts for models such as:
• Gemini 3.5 Flash
• Gemini 1.5 Pro
• Gemini 1.5 Flash

Since the calculation comes directly from Google's tokenizer, you can confidently estimate costs, monitor context usage, and compare tokenization across different AI providers.

Question 5

Can I compare token counts across multiple AI models at the same time?

Accepted Answer

Yes. TokenCounts lets you compare the same prompt across up to five AI models simultaneously.

By default, the tool compares several popular models, and you can customize the selection to include additional providers and open-source models.

This makes it easy to see:
• How different providers tokenize identical text.
• Which model is likely to be the most cost-effective.
• How tokenization varies for code, Markdown, PDFs, JSON, or multilingual content.
• How close each model is to its context window limit.

Side-by-side comparisons are especially useful when deciding which model to use in production or estimating API costs before deployment.

Question 6

How are PDF files tokenized?

Accepted Answer

When you upload a PDF, the document is first processed to extract its textual content.

Once the text has been extracted, it is sent to the selected AI provider's official token counting API, which returns the exact number of input tokens for that model.

This approach works well for:
• Research papers
• Technical documentation
• Books
• Contracts
• Reports
• Manuals

Because tokenization happens after text extraction, the results closely match what the AI model will actually process when you submit the document.

Question 7

How are image tokens calculated for OpenAI, Claude, and Gemini?

Accepted Answer

Vision models use different methods to calculate image tokens, which is why the same image can have different token counts across providers.

When you upload an image, TokenCounts sends it directly to each provider's official API so that token usage is calculated using the provider's own implementation.

Some examples include:
• OpenAI calculates image tokens using tiled image regions based on image dimensions.
• Anthropic Claude determines token usage using its own resolution-aware processing.
• Google Gemini applies its own image tokenization strategy for multimodal inputs.

Since these calculations are performed by the official APIs, the reported values match what each provider will use when processing your image.

Question 8

How much do 1,000 AI tokens cost?

Accepted Answer

The cost of 1,000 tokens depends on the provider and the model you choose.

Different models have different pricing for:
• Input tokens
• Output tokens
• Cached tokens
• Long-context requests

For example, lightweight models generally cost a fraction of a cent per thousand tokens, while frontier reasoning models can cost significantly more.

Because pricing changes regularly and varies across providers, TokenCounts includes a pricing calculator that uses the latest supported model prices to estimate your expected API costs.

Question 9

Is TokenCounts free to use?

Accepted Answer

Yes. TokenCounts is completely free to use.

You simply connect your own API keys from supported AI providers such as OpenAI, Anthropic, or Google, and the application performs token counting directly through their official APIs.

There are:
• No subscription fees.
• No hidden charges.
• No premium token limits.
• No paywall for core functionality.

You only pay the API provider according to your own account's usage and pricing plan.

Question 10

Are my API keys and prompts secure?

Accepted Answer

Yes. Security and privacy are built into the design of TokenCounts.

Your API keys are stored locally in your browser using local storage and are never uploaded to TokenCounts servers.

When you calculate tokens:
• Requests are sent directly from your browser to the official provider APIs.
• Your prompts are not stored by TokenCounts.
• Uploaded documents and images are not retained by the application.
• No third-party analytics or intermediary services process your content during token counting.

This architecture minimizes data exposure while allowing you to use each provider's official token counting service.

Question 11

What are the context window limits for modern AI models?

Accepted Answer

A context window is the maximum number of tokens an AI model can process in a single request. It includes your prompt, conversation history, uploaded content, and the model's generated response.

Different models support different context sizes.

Larger context windows make it possible to work with:
• Long technical documents
• Entire codebases
• Books
• Large PDFs
• Extended conversations
• Multi-file projects

Because context limits vary across providers and new models are released frequently, it's always a good idea to verify the supported limit before deploying an application that relies on long-context inputs.

Question 12

Why use TokenCounts instead of a generic AI token counter?

Accepted Answer

Many online token counters estimate token usage using rough formulas like "one word equals 1.3 tokens."

While these estimates may work for simple English sentences, they often become inaccurate for:
• Source code
• JSON
• Markdown
• PDFs
• Images
• Non-English languages
• Unicode characters
• Structured data

TokenCounts takes a different approach.

Instead of estimating, it uses the official APIs provided by OpenAI, Anthropic, and Google to calculate token usage directly from the source.

This gives you:
• More accurate token counts.
• Reliable cost estimates.
• Better context window planning.
• Consistent results across providers.
• Confidence that the reported numbers match what the model will actually use.

If accuracy matters—for pricing, prompt engineering, benchmarking, or production applications—using the provider's own token counting implementation is the most reliable approach.

AI Token Counter & Cost Calculator

Live Model Token Counts

GPT-5.6 Sol

Claude Sonnet 5

Gemini 3.6 Flash

Compare Up to 5 Models

PDF & Vision Image Support

100% Live Direct API Calls

Understanding AI Token Counting Across OpenAI, Claude, & Gemini

Why Use an OpenAI Token Counter?

Claude Token Counter & Anthropic Metrics

Gemini Token Counter & Multimodal Tokens

PDF and Multimodal Image Tokenization

Frequently Asked Questions