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JSON Token Counter - Count Tokens in JSON

Estimate the token count of your JSON data for GPT and other LLM models.

Input

json
loading editor…

Paste your JSON here...
or drop a file anywhere in this pane

Output

json

Token count will appear here...
press ⌘↵ to count tokens

What Is JSON Token Counter?

Token counting measures how many tokens your JSON data will consume when processed by large language models (LLMs) such as GPT-4, Claude, or Gemini. Since LLMs have context window limits and pricing is often token-based, knowing your token count is essential for optimizing prompts and managing costs.

This is used for:

  • LLM Cost Estimation: Calculate the token cost of JSON data before sending it to paid API endpoints.
  • Context Window Management: Ensure your JSON data fits within model context limits (e.g., 8K, 32K, or 128K tokens).
  • Prompt Optimization: Trim and optimize JSON data to reduce token usage while preserving essential information.

How to Use JSON Token Counter Online

  1. Paste your JSON data: Enter the JSON content you want to analyze into the input editor.
  2. Click Count Tokens: Press the Count Tokens button to estimate the token count.
  3. Review the results: View the estimated token count along with character count and other relevant metrics.

Frequently Asked Questions

How accurate is the token estimation?

The tool uses standard tokenization algorithms that closely approximate the tokenization used by major LLM providers. Actual token counts may vary slightly between different models and tokenizers.

Does the token counter work for any text or just JSON?

While designed for JSON content, the token counter works with any text input. The estimation algorithm counts tokens regardless of format, so you can use it for any text-based content.

Is this tool safe?

Yes. All processing happens 100% client-side in your browser. Nothing is uploaded to any server.

Is this tool free?

Yes, completely free with no usage limits or registration required.

Real-World Examples

AI engineers and prompt engineers use JSON Token Counter to optimize JSON data before feeding it into LLM pipelines. For example, when passing JSON configuration, database records, or API responses to an LLM for analysis, knowing the token count helps estimate API costs, avoid hitting context limits, and structure data efficiently for the best model performance.

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