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Your AI isn’t isolated. Your monitoring shouldn’t be either. See agent behavior in context — in the same Sentry already tracking your frontend, backend, and everything in between. For LLMs, tokens are the fundamental units of text that the model processes. When you type 'Hello world!' to ChatGPT, it doesn't see two words and punctuation, but rather it sees perhaps four distinct tokens: ['Hello', ' world', '!', '\n']. Tokens are what rule the world of LLMs. You send tokens to models, you pay by the token, and models read, understand, and breathe tokens.
What Are Tokens?Tokens are the fundamental units of text that an LLM processes. However, tokens are not always equivalent to words. Depending on the tokenization approach used, a token could represent:
For example, the sentence "I love machine learning!" might be tokenized as: ["I", "love", "machine", "learning", "!"] or ["I", " love", " machine", " learn", "ing", "!"] depending on the tokenization method. Why Tokenization MattersTokenization is very important for several reasons:
How Tokens are Read By LLMsOnce text is tokenized, there is one more step that transforms these symbolic tokens into something the neural network can actually process: numerical representation. Each token in the vocabulary is assigned a unique integer ID (called a token ID). For example. "Hello" → token ID 15496 " world" → token ID 995 These token IDs are then converted into high-dimensional numerical vectors called embeddings through an embedding layer. Each token ID maps to a dense vector of real numbers (typically 512, 1024, or more dimensions). For instance, the token "Hello" might become a vector like [0.23, -0.45, 0.78, ...]. This numerical transformation is necessary because neural networks can only perform mathematical operations on numbers, not on text symbols. The embedding vectors capture semantic relationships between tokens, where similar tokens have similar vector representations in this high-dimensional space. This is how models "understand" that "king" and "queen" are related, or that "run" and "running" share meaning. Common Tokenization Methods1. Byte Pair Encoding (BPE)BPE is one of the most widely used tokenization methods in modern LLMs, used by models like GPT-2, GPT-3, and GPT-4. How it works:
BPE creates a flexible subword vocabulary that efficiently represents common words while still being able to break down rare ones. This helps models handle misspellings, compound words, and unknown terms without resorting to an "unknown token." A key variant is byte-level BPE, which works directly with UTF-8 bytes rather than Unicode characters. This makes sure that any possible character can be represented (even those not seen during training), avoiding the "unknown token" problem.
2. WordPieceWordPiece was introduced by Google and is used in models like BERT, DistilBERT, and Electra. How it works:
For example, "unhappy" might be tokenized as ["un", "##happy"] in WordPiece. 3. SentencePieceSentencePiece is a tokenizer developed by Google that works directly on raw text without requiring language-specific pre-tokenization. It’s used in models like T5, XLNet, and ALBERT. How it works:
For example, the phrase "Hello world" might be tokenized as ["▁Hello", "▁world"], where the ▁ indicates a word boundary. 4. UnigramUnigram is often used together with SentencePiece and takes a probabilistic approach rather than a merge-based one. How it works:
Unlike BPE or WordPiece, which build their vocabulary by merging, Unigram works more like sculpting, starting big and pruning down. This allows it to maintain a broader set of tokenization options and gives it more flexibility during inference.
Tokens and Context WindowsLLMs have a limited "context window," which is the maximum number of tokens they can process at once. This limit directly affects:
Older models like GPT-2 were limited to ~1,024 tokens. GPT-3 increased this to 2,048. Today, cutting-edge models have limits of 1M+, such as Gemini 2.5 Pro. What To Know About TokenizationToken CountingUnderstanding token counts is important for:
As a rough estimate for English text (this varies!):
Tokenization QuirksTokenization can lead to some unexpected behaviors:
How Tokenization Impacts LLM PerformanceMany challenges and quirks in large language models stem not from the model itself, but from how text is tokenized. Here’s how tokenization affects different areas of performance:
The Infamous 3.11 vs 3.9 ProblemLarge language models often fail at seemingly simple numerical comparisons like “What is bigger: 3.11 or 3.9?”. Tokenization provides insight into how numbers are processed under the hood. Let's look at these numbers: 3.11 and 3.9. When tokenized, these are broken into separate components. For simplicity, let's say that “3.11” is split into tokens like "3", ".", and "11", while “3.9” is split into "3", ".", and "9". To a language model, these aren’t numerical values but symbolic fragments. The model isn’t comparing 3.11 against 3.9 as floating-point values. It’s pattern-matching based on the statistical likelihood of what text should come next, given how it has seen these tokens appear in its training data. There are multiple ways for models today to answer these correctly:
ConclusionTokenization is how LLMs break down text into processable units before converting them to numbers. Text like "Hello world!" becomes tokens like ['Hello', ' world', '!'], then gets converted to numerical vectors that neural networks can understand. Common methods include BPE (used by GPT models), WordPiece (BERT), and SentencePiece (T5). Tokenization directly impacts costs (you pay per token), context limits (models can only process so many tokens), and performance quirks. It explains why LLMs struggle with math (numbers get split up), why non-English text is less efficient (more tokens needed), and why models fail at "3.11 vs 3.9" comparisons (they see fragmented symbols, not numbers). Understanding tokenization helps you write better prompts, estimate API costs, troubleshoot issues, and grasp both the capabilities and fundamental limitations of modern AI. It gives you deeper insight into both the capabilities and limitations of modern AI, as it's the lens through which LLMs see everything. SPONSOR USGet your product in front of more than 1,000,000 tech professionals. Our newsletter puts your products and services directly in front of an audience that matters - hundreds of thousands of engineering leaders and senior engineers - who have influence over significant tech decisions and big purchases. Space Fills Up Fast - Reserve Today Ad spots typically sell out about 4 weeks in advance. To ensure your ad reaches this influential audience, reserve your space now by emailing sponsorship@bytebytego.com.
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