Prompt Token Counter
Measure prompt size across OpenAI and Claude workflows so long instructions, code, and source material stay practical.
OpenAI + Claude
Count prompt tokens before you paste them into a model
Paste any prompt, markdown draft, JSON payload, or code-heavy input and get client-side token counts, common context-fit checks, and prompt structure insights without sending content to a server.
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Other AI Token Counter Pages
Why use this token counter page?
A prompt token counter is useful whenever you are working with AI prompts that have grown beyond a quick chat message. That is increasingly common. Prompts now often include task framing, output rules, examples, structured input, markdown, code, JSON, and source material. Once prompts reach that level, word count stops being a reliable way to judge them. Token count becomes the more useful measure.
This is important because prompt quality is partly about size discipline. A long prompt is not automatically bad, but prompts that grow without structure often become harder to reuse and harder for a model to process cleanly. Counting tokens helps you see that before you send the request, not after the result feels noisy or unfocused.
That is what this page is built for. It gives you a practical token count for modern prompt workflows, compares that prompt size across OpenAI and Claude, and adds just enough structure insight to help you understand what is actually making the prompt heavy. That makes it useful for both technical and non-technical AI users.
Benefits of this workflow
Use a prompt token counter when you want one place to check prompt size before committing to a workflow. This is especially useful for long-form drafting, summarization, code review, structured extraction, and agent-style prompts where the input combines several different pieces of context. The tool helps you decide whether the prompt is still one sensible input or whether it should be split up.
This generic prompt-focused page is also valuable for search. Many users do not begin by searching for a provider. They search for the problem they are trying to solve: how many tokens is my prompt, count prompt tokens, or prompt token calculator. A variation page like this helps capture that earlier intent.
- Useful across both OpenAI and Claude workflows.
- Helps size long prompts before they become unwieldy.
- Makes token-heavy structures easier to spot.
- Good fit for prompt engineering, AI writing, coding, and research workflows.
How to use the tool well
Paste the full prompt you intend to use, including examples, format instructions, and source material. Check the raw token totals first, then move to the context-fit view to see whether the prompt is still comfortably inside common model windows. After that, use the prompt insights to understand whether code, markdown, JSON, or URLs are contributing heavily to the count.
If the prompt is larger than expected, simplify it with purpose. Remove repeated framing, separate background context from the core task, or chunk large source material into steps. The most useful result from a prompt token counter is not just the number itself. It is the decision you make from it.
Best practices
- Count the entire prompt you plan to send, not a shortened approximation.
- Review markdown, JSON, and code separately when prompts become unexpectedly large.
- Use staged prompts for very long or mixed-purpose tasks.
- Keep reusable prompt templates focused so they stay easy to maintain.
Frequently asked questions
Is token count more useful than word count for prompts?
Yes. Word count is a rough signal, but token count better reflects how models actually process prompt input.
What kinds of prompts benefit most from token counting?
Code review prompts, long content briefs, transcript summaries, structured extraction prompts, and any workflow where prompts include layered context.