AI Token Counter helps you work with AI and LLM prompts quickly and accurately, right in your browser. Whether you need to count tokens for gpt-4, gpt-3.5, claude, and llama models, this tool eliminatestoken waste, poorly structured prompts, and manual analysis by giving you instant, reliable results. Every operation runs locally on your device — nothing is uploaded to any server, so your data stays completely private.
Key Features
Multiple Models
Token counts for OpenAI GPT-4, GPT-3.5, Anthropic Claude, Meta Llama, and more.
Cost Estimation
Estimate API costs based on token count and model pricing.
Real-Time Counting
See token count update as you type or paste text.
Context Limit Check
Warns when approaching model context window limits.
Common Use Cases
- 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
AI Token Counter
Count tokens for GPT-4, GPT-3.5, Claude, and Llama models.
Frequently Asked Questions
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What is AI Token Counter?
AI Token Counter is a browser-based AI utility that count tokens for gpt-4, gpt-3.5, claude, and llama models. It provides Multiple Models, Cost Estimation, Real-Time Counting without sending any data to external servers. Your prompts, JSON schemas, and generated outputs remain entirely on your device.
AI workflows often require juggling multiple services — counting tokens here, formatting prompts there, converting outputs somewhere else. AI Token Counter brings these capabilities into your browser, eliminating the need to switch between tools or expose your prompts to third-party platforms. This is especially important when working with proprietary data, confidential business logic, or sensitive user information.
Frequently Asked Questions
How accurate is the token counter?
Approximate. Exact tokenization requires each model's tokenizer, but estimates are within 5-10%.
How does the Multiple Models feature work?
The Multiple Models feature token counts for openai gpt-4, gpt-3.5, anthropic claude, meta llama, and more. It is designed to be intuitive and responsive, giving you immediate feedback as you interact with the tool. All processing happens locally in your browser.
What is the benefit of cost estimation?
Cost Estimation estimate api costs based on token count and model pricing. This capability sets AI Token Counter apart from basic alternatives by providing more comprehensive functionality while maintaining the privacy and speed of local processing.
Is AI Token Counter really free to use?
Yes, AI Token Counter is completely free with no hidden charges, no sign-up requirements, and no usage limits. You can use it as often as you need, for any purpose — personal projects, commercial work, or educational use. There are no premium tiers or paid features.
Does AI Token Counter work on mobile devices?
Yes, it works on any device with a modern web browser — desktop, tablet, or phone. The interface is responsive and adapts to your screen size. Since all processing is done locally, you get the same performance regardless of your device.
What happens to my data when I use AI Token Counter?
Your data never leaves your device. Every operation is performed locally in your browser using JavaScript. No information is uploaded, stored, logged, or shared with any server. This privacy-first approach means you can work with sensitive data — passwords, API keys, personal information — without any risk of exposure.
Key Features
Multiple Models
Token counts for OpenAI GPT-4, GPT-3.5, Anthropic Claude, Meta Llama, and more.
Cost Estimation
Estimate API costs based on token count and model pricing.
Real-Time Counting
See token count update as you type or paste text.
Context Limit Check
Warns when approaching model context window limits.
Common Use Cases
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
Tips & Best Practices
Account for the response
Remember that the model's response also consumes tokens from the context window. Leave 500-1000 tokens of headroom for the response.
Use shorter prompts for cost savings
Every token costs money in API calls. Shorter, more concise prompts reduce costs and may produce more focused responses.
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