I built a simple but powerful command-line tool called Token Visualizer that helps you analyze the tokenization of your prompts across multiple LLM models — GPT-4, GPT-3.5, Claude, LLaMA, and more.
It breaks down token counts line-by-line, shows token efficiency (chars per token), highlights expensive prompt parts, and even suggests simple text compression tips to reduce your API usage costs.
The tool supports both the official OpenAI tokenizer (via tiktoken) and HuggingFace transformers tokenizers, and falls back gracefully to simple word splitting if those aren’t installed.
Why this matters:
Prompt token counts directly impact your API cost, latency, and model input limits.
Understanding how your prompt breaks down into tokens helps you optimize it for cost and performance.
It’s great for prompt engineers, developers, and AI researchers who want a transparent way to audit their prompt usage.
Features:
Multi-model tokenizer support with fallback
Line-level token analysis with color-coded cost heatmap
Token efficiency metrics
Token-by-token breakdown for smaller inputs
Suggestions for reducing token usage and verbose phrasing
Simple CLI interface, easy to integrate into your workflow
Try it out:
Run it interactively or pass a text file. It’s lightweight, no heavy dependencies unless you want richer tokenization support.
Would love to hear feedback on use cases, feature ideas, or integration suggestions. Also open to collaboration to turn this into a web-based tool or VSCode extension.
Hi HN,
I built a simple but powerful command-line tool called Token Visualizer that helps you analyze the tokenization of your prompts across multiple LLM models — GPT-4, GPT-3.5, Claude, LLaMA, and more.
It breaks down token counts line-by-line, shows token efficiency (chars per token), highlights expensive prompt parts, and even suggests simple text compression tips to reduce your API usage costs.
The tool supports both the official OpenAI tokenizer (via tiktoken) and HuggingFace transformers tokenizers, and falls back gracefully to simple word splitting if those aren’t installed.
Why this matters: Prompt token counts directly impact your API cost, latency, and model input limits.
Understanding how your prompt breaks down into tokens helps you optimize it for cost and performance.
It’s great for prompt engineers, developers, and AI researchers who want a transparent way to audit their prompt usage.
Features: Multi-model tokenizer support with fallback
Line-level token analysis with color-coded cost heatmap
Token efficiency metrics
Token-by-token breakdown for smaller inputs
Suggestions for reducing token usage and verbose phrasing
Simple CLI interface, easy to integrate into your workflow
Try it out: Run it interactively or pass a text file. It’s lightweight, no heavy dependencies unless you want richer tokenization support.
Would love to hear feedback on use cases, feature ideas, or integration suggestions. Also open to collaboration to turn this into a web-based tool or VSCode extension.