Is my data private when using AI Token Counter & Visualizer?

Yes. Code Station operates completely client-side without external telemetry, uploads, or server proxies. Your data never leaves your device.

Can AI agents use AI Token Counter & Visualizer?

Yes. AI Token Counter & Visualizer supports browser-native WebMCP (Web Model Context Protocol) and local stdio MCP servers via codestation-mcp for automated workflows.

AI & Prompt Engineering

AI Token Counter & Visualizer

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Station 25

AI & Prompt Engineering

In-browser token estimation, LLM JSON repair, prompt template interpolation, vector geometries, and pattern builder.

AI Token Counter & Visual Boundaries

Model-specific BPE estimation, alternating visual token boundaries, and API cost estimation.

Target Model Family

Input Price ($/1M tokens)

Output Price ($/1M tokens)

Text / Prompt to Tokenize394 characters

Total Tokens134~2.94 chars/token

Characters39456 words

Lines12paragraphs & code

Est. Input Cost$0.00034at $2.5/1M

Est. Output Cost$0.00134at $10/1M

Token Boundaries Breakdown (134 fragments)

You are a Senior TypeScript Architect at Code Station. Please review the following Next.js 15 App Router component: ```tsx export default async function DashboardPage() { const data = await fetchUserData(); return <UserMetrics data={data} />; } ``` Ensure it supports 100% in-browser WebMCP execution with zero telemetry. What are the performance implications? Don't forget edge cases! 🚀

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What does this tool do?

AI Token Counter & Visualizer: text or prompt to tokenize, target model family, input price ($/1m tokens), output price ($/1m tokens) → total token count, visual token boundaries, estimated api cost, character & word statistics. Process locally in your browser. Inputs: Text or prompt to tokenize, Target Model Family, Input Price ($/1M tokens), Output Price ($/1M tokens). Outputs: Total token count, Visual token boundaries, Estimated API cost, Character & word statistics.

How to use it?

Provide the listed input and choose Execute. Review the result, then copy or download where available. Clear removes inputs and results.

Browser-local processing. No uploads, content logging, persistence or remote resource loading. Generated markup is inert text; files are treated as untrusted data.

100% in-browser AI & prompt processing. Token estimation, JSON repair, vector metrics, and prompt construction run entirely in client memory with zero server uploads or external LLM API calls.

Inspect multi-model token counts across GPT-4o, Claude 3.5, Gemini 1.5, Llama 3, and generic BPE with visual token boundaries and cost estimation per 1M tokens.