LLM Tokens
Understanding how LLM tokens work in AI processing
What are LLM Tokens?
LLM tokens are the fundamental units that large language models use to understand and generate text. Think of them as the "atoms" of AI language processing. Every piece of text you send to an AI model gets broken down into these tokens before the model can process it.
Unlike words in human language, tokens don't always align with word boundaries. A token might be a whole word, part of a word, a single character, or even punctuation and spaces.
How Tokenization Works
AI models don't read text the way humans do. Instead, they use algorithms (like BPE - Byte Pair Encoding) to break text into smaller pieces. The most common words become single tokens, while rare or complex words get split.
Examples:
"Hello"→ 1 token (common word)"artificial"→ 1 token (common word)"tokenization"→ 2-3 tokens (less common)"NyxoChat"→ 3+ tokens (brand name, uncommon)"こんにちは"→ 3+ tokens (non-English characters)
Token Counting Rules
Here are some general rules for estimating token counts:
- English text: ~4 characters = 1 token (or ~0.75 words per token)
- Common words: Usually 1 token each
- Numbers: Each digit is often its own token
- Punctuation: Usually 1 token each
- Spaces: Often merged with adjacent words
- Code: Variable names and syntax use more tokens
- Non-English: Characters may use 2-4× more tokens
Input vs Output Tokens
When you interact with an AI model, there are two types of token usage:
- Input tokens: Your message, the conversation history, and any system instructions sent to the model
- Output tokens: The AI's response back to you
Both input and output tokens count toward the total usage. Longer conversations accumulate more input tokens as the history grows.
Context Window
Every AI model has a context window – the maximum number of tokens it can process at once. This includes your input AND the model's output.
- Smaller models: 4K - 8K tokens
- Standard models: 32K - 128K tokens
- Large context models: 200K - 1M+ tokens
* When a conversation exceeds the context window, older messages may be truncated or summarized
Tips for Efficient Token Usage
While NyxoChat handles token counting for you, being efficient still helps:
- Be concise: Clear, direct prompts work better than verbose ones
- Start fresh: Begin new conversations for unrelated topics
- Avoid repetition: Don't repeat information the AI already knows
- Use formatting wisely: Bullet points are often more efficient than paragraphs
- Be specific: Precise questions get precise (shorter) answers
Tokens in NyxoChat
In NyxoChat, you don't need to worry about counting tokens yourself. Our pricing is simplified:
- Standard Chat: Fixed per-message pricing in Comets
- Nebula: Every 5,000 AI tokens = 1 message price
We handle the complexity behind the scenes so you can focus on your conversations!