Free Token Counter for LLMs
Estimate token usage for major LLM families so you can plan context size and API cost before sending a request.
Token counts are estimates based on BPE approximation. Actual counts may vary slightly by model version.
About Token Counting
A fast client-side token estimator for the most popular LLM APIs. No data is sent to any server.
Why count tokens?
LLM APIs charge per token and enforce context window limits. Knowing your token count before sending a request helps you optimize prompts, avoid truncation, and control costs.
Key Features
- Multi-model Support: Estimates tokens for GPT-4, GPT-3.5, Claude, and Llama simultaneously.
- Real-time Counting: Token counts update instantly as you type.
- Context Bar: Visual progress bar shows how much of each model context window is used.
- File Import: Load .txt, .md, .json, or code files directly for batch counting.
How to Use
- Paste your prompt or text into the input area.
- View the estimated token count for each model in real time.
- Use the context bar to ensure your text fits within the model context limit.
Common Use Cases
- Estimate prompt size before calling an LLM API so you do not exceed the available context window.
- Compare approximate token usage across GPT, Claude, and Llama before deciding on budget or chunking strategy.
- Trim long prompts, system messages, or knowledge-base excerpts before wiring them into production code.
How To Read These Estimates
The numbers shown here are browser-side approximations rather than exact counts from each model's official tokenizer. They are useful for planning, chunking, and cost estimation, but final billing or strict truncation should still be checked with the model-specific tokenizer.
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Understanding Token Counts for LLMs - GPT, Claude, and LlamaA practical explanation of token counting, context windows, and cost estimation for modern LLM APIs.