AI GLOSSARY

Token

A token is the smallest unit into which language models break down text—usually a part of a word, a word, or a character. Tokens determine costs, latency, and capacity limits. Understanding tokens allows you to work more efficiently and cost-effectively with AI.

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3

Characters per token
an average of 3–4 in German

4

Cost factor
linear per request

3

Boundaries
Context windows in tokens

6

Best Practices
for Efficient Use

Why Tokens Are Crucial for AI Use

Tokens are the currency of language models. All costs, capacities, and latencies are measured in tokens. Understanding the basics saves money and helps build better AI applications.
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Cost Control

Each token costs—often millions per month. Awareness leads to significant savings.

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Context Planning

Models have token limits—documents must fit within them.

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Understanding Latency

More tokens mean longer response times—which is important for UX.

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Budget Forecast

Without an understanding of tokens, there can be no reliable cost forecast.

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Prompt Optimization

Shorter, more targeted prompts are more cost-effective—minimize the number of tokens.

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Choose Your Model More Carefully

Different models have different tokenizers—compare them.

What is a token?

A token is the smallest processing unit in AI language models. Text is broken down into tokens by a tokenizer—depending on the model and language, these are usually parts of words, whole words, or individual characters. The model works exclusively with tokens, not with text.

Typical size ranges: English: 1 token ≈ 4 characters or 0.75 words; German: 1 token ≈ 3–4 characters (less efficient than English); Chinese: 1 token ≈ 1–2 characters; code: variable depending on language and structure.

Key aspects: Context window (maximum tokens per request—4k to over 1 million), input and output tokens (often priced differently), prompt caching (repeated tokens are cheaper), model-specific tokenizers (GPT and Claude tokenize differently).

For small and medium-sized businesses, token awareness is practically relevant: If you don’t pay attention to tokens when using APIs, you’ll end up with hefty bills. Those who optimize effectively (prompt length, RAG chunks, caching) can often reduce costs by a factor of 3 to 10. Token counters and cost estimators are essential for every AI project.

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Token Techniques in Detail

These eight concepts will help you work with tokens:

BPE (Byte Pair Encoding)

The standard tokenizer used by GPT and many others. Efficient and robust.

SentencePiece

Alternative Approach — by Claude, Gemini, and others.

Tokenizer Libraries

tiktoken (OpenAI), transformers (Hugging Face) — Count tokens in advance.

Prompt Caching

Repeated prompt sections are billed at a lower rate—save big.

Chunking

Split long texts into appropriate token blocks — for RAG and processing.

Streaming

Tokens are delivered individually — for a better user experience with lower latency.

Token Limits

Be aware of and adhere to model limits—otherwise, errors or truncation may occur.

Cost Estimate

Before deployment, calculate the application's monthly cost.

Best Practices for Token Usage

These six principles have proven effective:

  • Precise prompts: Vague prompts require longer answers—formulate them specifically.
  • UseRAG instead of extensive context: Include only relevant chunks rather than entire documents.
  • Use prompt caching: Implement it immediately for recurring system prompts.
  • Control output length: Set a maximum number of tokens—to manage costs.
  • Use German thoughtfully: German texts require more tokens—use them only where necessary.
  • Choose the right model: Not always the largest—smaller ones are often sufficient.
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Language 1

English

Efficient — 1 token ≈ 4 characters, 0.75 words. The basis for many pricing models.

Efficient

Language 2

German

Less efficient — 1 token ≈ 3–4 characters. Compound words dominate.

Longer

Language 3

Chinese

Very dense — 1 token ≈ 1–2 characters. Costs less per unit of meaning.

Dense

Common Mistakes with Tokens

We often see these pitfalls:

  • Token limit exceeded: Prompt too long—error or truncation without warning.
  • Costs underestimated: A few cents per request — but with millions of requests, that adds up to thousands of euros.
  • No prompt caching: Missing out on massive savings with recurring prompts.
  • Output limit forgotten: Model responds with a novel — expensive and slow.
  • German language inefficiency ignored: German requires more tokens than English—stay mindful of this.

Tokens vs. Words vs. Characters

A comparison of three text units:

  • Character: Smallest unit—the basis for some tokenizers.
  • Token: Language model unit — usually parts of words.
  • Word: Natural unit — not identical to tokens.
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Frequently Asked Questions About Tokens

Optimize Token Usage with prodot

In a free initial consultation, we’ll analyze your token usage and identify potential savings—typically a 2x to 5x reduction is possible.

As an AI partner for small and medium-sized businesses, we build efficient AI applications—with a clean token economy and active monitoring.

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