AI token counter. Count tokens for GPT-4, GPT-3.5, Claude, and Llama models with cost estimation and context limit checking.
100% browser-based — your data never leaves your device
Count tokens for GPT-4, GPT-3.5, Claude, and Llama models.
Type or paste the text you want to count tokens for into the input area.
Choose the AI model (GPT-4, GPT-3.5, Claude, Llama) to get model-specific token counts.
View the estimated token count and API cost for your selected model.
Verify your text fits within the model's context window and adjust if necessary.
Ctrl+Enter to count tokens
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AI Token CounterCount tokens for GPT-4, GPT-3.5, Claude, and Llama models.
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AI Token Counter is a free online ai tool that count tokens for gpt-4, gpt-3.5, claude, and llama models. It works entirely in your browser — no downloads, no installations, and no server uploads required. prompt engineers, AI researchers, full-stack developers use AI Token Counter when they need a fast, privacy-respecting way to process ai data without installing additional software. It supports capabilities such as multiple models, cost estimation, real-time counting. Additional features include context limit check. Common use cases include estimating api costs before making large-scale llm api calls, ensuring prompts fit within model context windows to avoid truncation, optimizing token usage to reduce costs in production ai applications. All processing happens locally using modern browser APIs, which means your data never leaves your device. This makes AI Token Counter ideal for working with sensitive data, proprietary code, or any situation where privacy matters.
AI Token Counter is a free online ai tool that helps you count tokens for gpt-4, gpt-3.5, claude, and llama models. Key capabilities include multiple models, cost estimation, real-time counting. All processing is done locally in your browser — no data is uploaded, stored, or shared with any server. There are no sign-ups, no file size limits, and no hidden charges. It is commonly used to estimating api costs before making large-scale llm api calls.
Type or paste the text you want to count tokens for into the input area.
Choose the AI model (GPT-4, GPT-3.5, Claude, Llama) to get model-specific token counts.
View the estimated token count and API cost for your selected model.
Verify your text fits within the model's context window and adjust if necessary.
| Category | Details |
|---|---|
| Input Limits | Maximum prompt length: Browser memory bound |
| Token estimate precision: ±5% (heuristic) | |
| Supported Formats | Plain text, JSON, Structured prompts, Token counts |
| Browser Engine |
|
All operations run entirely in your browser using built-in AI APIs. No server uploads, no data storage, and no third-party requests.
AI Token Counter is ideal when you need to estimating api costs before making large-scale llm api calls without installing software or uploading data to a server. Since everything runs in your browser, there is no setup time and no risk of your data being stored on external servers. For one-off tasks and quick processing, AI Token Counter is faster than setting up a local development environment or using a command-line tool.
No. AI Token Counter processes everything entirely in your browser using JavaScript. Nothing you type, paste, or upload is ever transmitted to any server. Your data never leaves your device.
Approximate. Exact tokenization requires each model's tokenizer, but estimates are within 5-10%.
No. All AI prompt formatting, token counting, and analysis happens locally in your browser. Your prompts and data never leave your device. No API calls are made to external AI providers.
Token counts for OpenAI GPT-4, GPT-3.5, Anthropic Claude, Meta Llama, and more.
Estimate API costs based on token count and model pricing.
See token count update as you type or paste text.
Warns when approaching model context window limits.
AI Token Counter is useful in a variety of scenarios across different workflows:
Estimating API costs before making large-scale LLM API calls
Ensuring prompts fit within model context windows to avoid truncation
Optimizing token usage to reduce costs in production AI applications
Remember that the model's response also consumes tokens from the context window. Leave 500-1000 tokens of headroom for the response.
Every token costs money in API calls. Shorter, more concise prompts reduce costs and may produce more focused responses.
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