Token: What is a Token in Artificial Intelligence?

What is a Token?

A token is the fundamental unit of text that language models (LLMs) use to read, understand, and generate content. Before processing text, the model splits it into tokens through a process called tokenization. A token does not necessarily correspond to a word: it can be a complete word, a syllable, a prefix, a suffix, or even a single character, depending on the tokenization algorithm used.

In practice, for French or English text, a token corresponds on average to about 3 to 4 characters, roughly three-quarters of a word. For example, the word 'intelligence' might be split into two tokens ('intelli' and 'gence'), while short words like 'the' or 'is' each form a single token. Numbers, punctuation, and spaces also consume tokens. Non-Latin languages (Chinese, Arabic, Japanese) typically use more tokens per word.

The most common tokenization algorithms are BPE (Byte-Pair Encoding), used by GPT-4 and Claude, and SentencePiece, used by many open-source models. These algorithms build a vocabulary of frequent subwords from the training corpus, optimizing the balance between vocabulary size and representation efficiency.

Why Tokens Matter

Tokens are at the heart of LLM economics and performance. Understanding them is essential for anyone integrating AI into their applications.

• Pricing: LLM providers (OpenAI, Anthropic, Google) charge based on the number of tokens processed, often distinguishing between input tokens (the prompt) and output tokens (the response). Mastering token counting is essential for forecasting and controlling costs.
• Context window: each model has a token limit it can process in a single request. Claude can handle up to 200,000 tokens, GPT-4 Turbo up to 128,000. Exceeding this limit means the model cannot 'see' all the provided context.
• Response quality: how text is tokenized affects model comprehension. A well-structured prompt uses tokens efficiently, while a verbose prompt wastes precious context space.
• Latency: the more tokens to generate, the longer the response takes. Each token is generated sequentially, so inference speed is measured in tokens per second.
• Optimization: understanding tokens allows prompt optimization for better responses at lower cost, a critical challenge for high-volume applications.

How It Works

Tokenization transforms a text string into a sequence of integers, each corresponding to an element in the model's vocabulary. The BPE algorithm starts by treating each character individually, then iteratively merges the most frequent character pairs in the training corpus to form increasingly long subwords. The result is a vocabulary of typically 32,000 to 100,000 tokens that efficiently covers the most common words and subwords.

When a user sends a prompt to an LLM via an API, the text is first tokenized into a sequence of token IDs. The model processes this sequence through its neural layers (Transformer) and generates output tokens one by one, each conditioned on all preceding tokens. The process repeats until the model produces an end-of-sequence token or reaches the configured token limit.

Special tokens play a crucial role: the beginning-of-sequence token, end-of-sequence token, role separator tokens (system, user, assistant), and padding tokens structure communication with the model. Each provider uses different special tokens, which is why prompts are not directly transferable between models without adaptation.

Concrete Example

At KERN-IT, fine-grained token management is a central aspect of optimizing AI solutions developed by KERNLAB. For the A.M.A assistant, the team implemented a token budget system that dynamically allocates context space between the system prompt, RAG-retrieved documents, and conversation history. When a user asks a question about a large document, the system selects the most relevant passages to stay within the context window while maximizing response quality.

A concrete case: for a client whose support agents use an AI assistant, KERN-IT reduced API costs by 40% through prompt tokenization optimization. Techniques employed include system instruction compression, caching of frequent conversation prefixes, and intelligent history truncation. The result: responses of the same quality at significantly lower cost, allowing usage volume to increase without blowing the budget.

Implementation

1. Count tokens: use tokenization libraries (tiktoken for OpenAI, Anthropic tokenization) to precisely estimate token consumption before sending requests.
2. Optimize prompts: write concise, structured instructions. Eliminate redundancies, use lists rather than verbose paragraphs, and test different formulations.
3. Manage the context window: implement a truncation or summarization strategy for long conversations. Prioritize the most recent and relevant information.
4. Monitor costs: set up a dashboard tracking tokens consumed per endpoint, per user, and per request type to identify possible optimizations.
5. Cache results: for frequent or similar queries, implement a semantic cache that avoids reconsuming tokens for previously handled questions.
6. Choose the right model: use smaller, cheaper models for simple tasks (classification, extraction) and reserve powerful models for complex tasks (reasoning, writing).

Associated Technologies and Tools

• Tokenizers: tiktoken (OpenAI), SentencePiece (Google), Hugging Face Tokenizers for counting and analysis
• Optimization: prompt compression (LLMLingua), semantic caching (GPTCache), prefix caching (native at Anthropic)
• Monitoring: LangSmith, Helicone, Portkey for detailed token consumption and cost tracking
• APIs: OpenAI API, Anthropic API, Google Vertex AI with per-token billing
• Calculators: OpenAI Tokenizer, Anthropic Token Counter for pre-estimation of costs

Conclusion

Tokens are the currency of generative AI. Understanding them means mastering costs, optimizing performance, and getting the best out of LLMs. KERN-IT and KERNLAB integrate this expertise into every AI project, ensuring deployed solutions are not only performant but also economically viable. In a context where query volumes are increasing and every token has a cost, tokenization optimization is a real competitive advantage for companies integrating AI at scale.