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  5. Token (LLM Context)

Token (LLM Context)

In the context of large language models, a token is the basic unit of text the model processes — roughly a word fragment, word, or punctuation mark — used to measure both input length and output length, with practical limits imposed by the model's context window.

Last reviewed: 2026/05/19

Definition

Why It Matters for Lawyers

How AI Tools Handle It

Frequently Asked Questions

Q1: How can I tell if a document is too long for a legal AI tool to process accurately?
Most tools have disclosed maximum input lengths. As a practical test, include a specific fact early in a long document and then ask the tool about it near the end of the session. If the tool cannot accurately recall or reference the early information, it may be operating at or near its context limit. For critical matters, limit document-analysis sessions to within the tool's specified capacity.
Q2: Does the context window fill up with conversation history as well as documents?
Yes. In a multi-turn AI conversation, each previous exchange consumes context window tokens. A tool that has processed a 50,000-token contract and then engaged in 20 exchanges has used substantially more of its context window than a fresh session analyzing only the contract. Some tools manage this by summarizing or compressing prior context; others reset the context after a limit is reached.
Q3: Are tokens the same across all LLM providers?
No. Different models use different tokenization schemes, so the same text may convert to different token counts across providers. As a rough guide, 1,000 tokens corresponds to approximately 750 English words. Legal text, which tends to have longer words and complex sentence structures, typically tokenizes at a somewhat higher ratio than general prose. --- *Last reviewed: 2026-05-19 by LawyerAI Editorial Team.*

Related Concepts

Tech / Model

Context Window

The context window is the maximum amount of text — measured in tokens — that a large language model can process at one time, determining how much document content, conversation history, and instructions the model can consider when generating a response.

Tech / Model

LLM (Large Language Model)

A large language model (LLM) is an AI system trained on large volumes of text data to predict and generate human-like text; it serves as the core engine underlying most legal AI tools for research, drafting, and document analysis.

Tech / Model

Inference

In AI, inference is the process of running a trained model to generate outputs from new inputs — as distinct from training, which creates the model. Every time a lawyer submits a query to a legal AI tool, inference occurs.

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Last reviewed: 2026/05/19. Definitions are written by the LawyerAI Editorial team. We do not accept affiliate commissions; Featured placement is clearly labeled and does not influence editorial content.

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© 2026LawyerAI Editorial

In the context of large language models, a token is the basic unit of text the model processes — roughly a word fragment, word, or punctuation mark — used to measure both input length and output length, with practical limits imposed by the model's context window.

Tokens define the practical limits of what a legal AI tool can process in a single session. A typical English word is approximately 1.3 tokens. A 100-page contract is roughly 25,000–35,000 tokens. A model with a 128,000-token context window can process the entire contract in one pass; a model with a 4,000-token limit would need the document to be chunked into sections, potentially losing context across chunks.

For lawyers using AI tools on large documents — lengthy contracts, voluminous discovery productions, multi-volume deposition transcripts — the token capacity of the underlying model directly affects whether the tool can process the entire document or must operate on fragments. Processing fragments can cause the AI to miss connections across document sections, such as a defined term introduced in one section that modifies an obligation in another.

Token limits also affect cost: most AI APIs charge by the number of tokens processed. Tools that maintain conversation history accumulate tokens with each exchange, eventually hitting context limits that force the session to restart.

Understanding token constraints helps lawyers evaluate whether a tool is suitable for large-document tasks and whether a tool's behavior on long documents (such as apparent failure to "remember" earlier document content) is a context limit issue.

Legal AI tools manage token limits through different approaches. Tools designed for document-length analysis use chunking strategies — dividing large documents into overlapping segments for processing — with the trade-off that cross-segment context can be lost. More capable tools use longer context models to reduce the need for chunking.

Recent advances in context window size have significantly expanded what's practical. Models with context windows of 128,000 or 200,000 tokens can process substantial legal documents in full — a 200-page acquisition agreement, a multi-day deposition transcript — without chunking.

Harvey AI and similar enterprise legal tools typically build on models with large context windows specifically to address the document-length requirements of legal work. Tool documentation or vendor materials should specify the effective context limit for document analysis tasks.

For e-discovery workflows involving very large document collections, individual document-level token limits are less relevant than the platform's batch processing architecture, which handles collection-level analysis outside a single context window.