⚡ LLM Tokenizer Arena & Context Inspector

Select Model Tokenizer

Vocabulary size: 152,064 tokens. Exceptional multilingual & code compression.

Input Text to Inspect

Quick Presets

Tokens

Characters

Bytes (UTF-8)

Chars / Token

0.0

Visual Token Breakdown

Hover chips for token details • Whitespaces shown as ·

Enter text or select a preset to visualize tokens.

View Detailed Token ID Table (0 items)

#	Token ID	String	Length	Bytes

🏁 Multi-Model Compression Arena

Compare how efficiently each model's vocabulary compresses your text. Fewer tokens = faster generation, reduced cost, and longer effective context memory.

Token Count Comparison (Fewer is Better)

Efficiency Ranking

Model	Tokens	Chars/Tok

Run benchmark to view efficiency insights.

🧮 KV-Cache & GPU VRAM Sizing Calculator

Calculate exact Key-Value cache memory consumption and total GPU memory footprint across context lengths (1k to 128k+) with Grouped Query Attention (GQA) and Multi-Head Latent Attention (MLA).

Model Architecture Preset

Context Length (Tokens) 16,384 tokens

1k 32k 64k 128k

Concurrency / Batch Size 1

KV Cache Precision

Weights Precision

KV Cache Memory

0.50 GB

Model Weights

16.0 GB

Total VRAM Needed

18.1 GB

Hardware Sizing & Compatibility

GPU / Hardware	VRAM	Status	Headroom

Grouped Query Attention (GQA) reduces KV memory by 4x to 8x compared to Multi-Head Attention.

⚡ LLM Tokenizer Arena & Context Inspector

Visual Token Breakdown

🏁 Multi-Model Compression Arena

Token Count Comparison (Fewer is Better)

Efficiency Ranking

💬 Prompt Template Configuration

Raw Assembled Prompt with Delimiters

🧮 KV-Cache & GPU VRAM Sizing Calculator

About LLM Tokenizer Arena

Why Vocabulary Size Matters

GQA & MLA: KV-Cache Innovations