README.md

metadata

title: Token Playground
emoji: 🎮
colorFrom: yellow
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
license: mit

Token Playground

Question

How does prompt length translate into model cost?

System Boundary

This Space is a lightweight model-economics tool. It estimates token counts and rough costs across model families so users can reason about prompt size before deployment.

Method

The app applies simple tokenizer approximations, maps token counts to model pricing assumptions, and displays cost estimates in a compact comparison panel.

Technique

Token accounting is the measurement layer of LLM systems. Every prompt, retrieved context window, tool trace, and model response becomes tokens.

The app keeps the model simple so the relationship is visible: longer text increases tokens, tokens drive cost, and cost changes architecture decisions.

Output

The app returns estimated token counts and approximate request costs.

Why It Matters

Many LLM systems fail economically before they fail technically. Token literacy is part of engineering literacy.

What To Notice

Small prompt changes can matter at scale. A few hundred extra tokens are negligible once, but significant over millions of calls.

Effect In Practice

Token awareness informs prompt compression, retrieval limits, model routing, caching, and evaluation of latency/cost tradeoffs.

Hugging Face Extension

The Space can be extended with real tokenizers from open models and a comparison of local inference versus hosted API economics.

Limitations

The estimates are approximate. Exact billing requires the provider tokenizer, current pricing, caching behavior, batching behavior, and input/output token separation.

Run Locally

pip install -r requirements.txt
python app.py