Encyclopedia

A context window in artificial intelligence, particularly in large language models (LLMs), refers to the maximum amount of text a model can process and consider at once when generating responses.¹² Measured in tokens, the context window serves as a form of "working memory" for AI models, determining how much information they can retain and reference during interactions.³⁴ As LLMs have evolved, context window sizes have dramatically increased from just a few thousand tokens to millions, enabling more sophisticated applications and improved performance across complex tasks.⁵⁶

Overview

The context window represents the total span of tokens (words, subwords, or characters) that an AI model can access and process simultaneously.⁷ This includes both the input prompt (user queries, documents, or instructions) and the model's generated output. When a conversation or document exceeds the context window's capacity, the model loses access to earlier content, potentially leading to inconsistencies or loss of relevant information in responses.²⁸

Context windows function similarly to human short-term memory—they allow the model to "look" at and reference a specific amount of information at once, beyond which earlier content begins to "fade" as new information is processed.¹⁹ This limitation affects how well models can maintain coherence over lengthy interactions or process extensive documents.¹⁰

The size of a context window is typically measured in tokens rather than words. Tokens are the smallest units of data that language models process and may represent words, parts of words, punctuation marks, or other linguistic elements.⁹¹¹ For example, in the sentence "It's sunny!", tokens might be represented as ["It's", "sunny", "!"].⁹

Technical aspects

Tokenization and positional encoding

For language models to process text effectively within their context windows, two fundamental processes are involved: tokenization and positional encoding.

Tokenization

Tokenization is the process of breaking text into smaller units (tokens) that the model can process.⁹ Different language models use different tokenization methods, resulting in varying token counts for the same text. Tokenization affects how efficiently a model can use its context window, as inefficient tokenization might require more tokens to represent the same information.⁷¹¹

Positional encoding

Positional encoding helps models understand the order and relationships between tokens in a sequence.⁹¹² Without positional information, transformer-based models would treat text as an unordered collection of tokens, losing critical syntactic and semantic relationships. Positional encoding creates a mathematical pattern (often using sine and cosine functions) that assigns a unique position to each token, allowing the model to distinguish between sentences like "The cat sat on the mat" and "The mat sat on the cat."⁹¹²

Different approaches to positional encoding include:

• Absolute positional encodings: Assign a unique embedding to each position¹²
• Relative positional encodings (RoPE): Add biases based on the relative distance between tokens¹²
• ALiBi: Apply larger negative biases to attention based on relative distance¹²

Attention mechanism

The context window is closely tied to the attention mechanism in transformer-based models. Attention allows each token to "attend" to all other tokens in the sequence, establishing relationships between different parts of the text.¹² However, this process becomes computationally expensive as the sequence length increases, because the computational requirements grow quadratically with the number of tokens.⁵¹³

When a sequence doubles in length, the memory and computational needs quadruple, creating significant challenges for extending context windows.¹³⁷ This quadratic scaling relationship is a key constraint in developing models with larger context windows.⁵

Evolution of context window sizes

Context window sizes have experienced dramatic growth since the introduction of large language models:

• 2022: Early models like GPT-3 had limited context windows of approximately 2,048 tokens (about 1,500 words)¹¹⁴
• 2023: Models began to expand significantly, with Claude announcing support for 100,000 tokens¹
• 2024: Context windows reached millions of tokens, with Google's Gemini 1.5 Pro supporting 2 million tokens (approximately 3,000 pages of text)¹⁵
• 2025: Magic claimed a context window of 100 million tokens, enough to fit approximately 750 novels¹⁴

This rapid expansion has been driven by architectural innovations, improved training techniques, and hardware advancements that enable more efficient processing of long sequences.⁷¹³

Evolution timeline

Impact and importance

Larger context windows have transformed the capabilities of language models in several key ways:

Extended reasoning and information processing

Expanded context windows allow models to process and reason over substantially more information at once.⁵ This enables more sophisticated reasoning across lengthy documents, improved summarization capabilities, and enhanced performance on complex tasks that require maintaining context over extended interactions.¹⁵

Improved in-context learning

Larger context windows facilitate "many-shot" in-context learning, where models can learn from hundreds or thousands of examples provided directly in the prompt.⁵ This approach can help models adapt to new tasks without requiring fine-tuning, improving their versatility and adaptability.⁵

Enhanced document analysis

Models with expanded context windows can analyze entire documents, codebases, or datasets in a single pass without losing important connections between distant elements.⁵⁷ This capability is particularly valuable for:

• Analyzing lengthy legal contracts and regulatory documents⁵⁹
• Processing complete medical histories and research papers⁵⁹
• Understanding and working with large codebases⁵⁹
• Analyzing financial reports and market research⁵⁹

Multimodal applications

Advanced models with large context windows can process multiple modalities together, enabling applications that involve text, images, audio, and video simultaneously.⁵ This capability supports tasks like video analysis, real-time transcription and translation, and multimedia content generation.⁵

Challenges and limitations

Despite their advantages, larger context windows present several significant challenges:

Computational complexity

The quadratic relationship between sequence length and computational requirements creates substantial processing demands as context windows expand.⁵⁷¹³ When a text sequence doubles in length, an LLM requires four times as much memory and computation to process it, leading to increased inference times and resource utilization.⁷¹³

Cost implications

The increased computational demands of larger context windows translate directly into higher operational costs.¹⁷⁹ For example, on a pay-per-query basis, processing a document with a context window of 128,000 tokens can be significantly more expensive than processing one with 4,000 tokens.⁹ This cost consideration is particularly important for applications with high query volumes.⁹

The "murky middle" problem

As context windows expand to handle entire books or extensive documents, models often struggle with what has been termed the "murky middle" problem.⁹ This phenomenon occurs when critical details buried in the middle of long texts are overlooked by the model, which tends to focus more effectively on information positioned at the beginning or end of the context window.⁹¹²

Attention dilution

Larger context windows can lead to attention dilution, where the model's focus is spread too thinly across an extensive input.¹³ Research indicates that providing a focused set of relevant documents often yields better performance than inundating models with excessive unfiltered information.¹³

Context window sizes in popular models

Modern language models feature a wide range of context window sizes, reflecting different architectural choices and intended use cases:

Llama 4 Scout, released in April 2025, features one of the largest context windows among publicly available models, supporting up to 10 million tokens.¹⁵ This extensive context capacity enables advanced capabilities including multi-document summarization, parsing extensive user activity, and reasoning over large codebases.¹⁵

Methods for extending context windows

Researchers have developed various approaches to overcome the limitations of fixed context windows and enable models to process longer sequences:

Architectural innovations

Ring attention

This technique improves computational efficiency by optimizing how attention is calculated over long sequences.⁷¹³ It reduces memory requirements and enables more efficient processing of extended inputs.¹³

iRoPE (interleaved RoPE)

The iRoPE architecture removes positional embeddings from some attention layers, allowing for better length generalization and more efficient handling of long sequences.¹⁵

Parallel context windows (PCW)

This approach breaks long text sequences into smaller chunks, with each chunk operating within its own context window while reusing positional embeddings.¹² This method allows models to process extensive text without retraining, making it scalable for various tasks.¹²

Training techniques

Position-wise training

Techniques like Positional Skip-wise Training (PoSE) adjust how models interpret positional data by dividing text into chunks and using skipping bias terms to simulate longer contexts.¹² This approach extends a model's ability to process lengthy inputs without increasing computational load.¹²

Dynamic in-context learning (DynaICL)

This method enhances how LLMs use examples to learn from context by dynamically adjusting the number of examples based on task complexity.¹² A meta-controller predicts the optimal number of examples, reducing token usage while improving performance.¹²

Parameter adaptation

Dynamic NTK scaling

This approach modifies the base parameter in relative positional encodings like RoPE to extend a model's ability to handle sequences beyond its training length.¹²

Attention scaling

By multiplying attention logits by a scaling factor, this method can effectively interpolate positional vectors and extend a model's context window without retraining.¹²

Positional vector replacement

This technique replaces implicitly learned positional vectors with interpolated ones to avoid issues with out-of-distribution positions when exceeding the original context window.¹²

Context windows and retrieval-augmented generation

As context windows have expanded, their relationship with retrieval-augmented generation (RAG) has evolved from competitive to complementary:

RAG as an alternative

RAG was initially developed as an alternative to large context windows, allowing models to dynamically retrieve relevant information from external sources rather than requiring all data to fit within a limited context.⁵¹³¹⁴ This approach helped overcome early context window limitations by enabling access to broader knowledge without overwhelming the model.⁹¹³

Complementary approaches

More recent research suggests that RAG and long context windows can work together effectively:¹⁴

• LongRAG: Combines longer retrieval chunks (of at least 4,000 tokens) with long-context LLMs to improve performance on specific information retrieval tasks¹⁴
• Hybrid systems: Use RAG for efficient dynamic retrieval of current information while leveraging long context for coherent processing of retrieved data¹³¹⁴
• Contextual filtering: RAG can help filter and prioritize the most relevant information to include within a model's context window¹³

The combination of these approaches can address different use cases more effectively than either approach alone:¹⁴

• RAG excels at retrieving up-to-date information and specific facts
• Long context windows are better at maintaining coherence and understanding complex relationships across extensive content
• Together, they can maximize both accuracy and efficiency

Evaluation methodologies

New evaluation methods have been developed to assess long context capabilities:¹⁴

• Needle in a Haystack: Traditional tests insert unique phrases in long documents to test retrieval
• HashHop: An advanced evaluation method developed by Magic that tests a model's ability to recall random hashes in documents up to 100 million tokens in length¹⁴

Future directions

The development of context windows continues to evolve, with several key trends shaping their future:

Increased efficiency

Research is focusing on reducing the quadratic computational scaling of attention mechanisms to make larger context windows more practical and cost-effective.⁷¹³ Techniques like sparse attention, linear attention, and memory-augmented architectures aim to address this challenge.¹²¹³

Memory-augmented models

By incorporating external memory systems, models like MemGPT can store information externally and retrieve it when needed, mimicking how computers manage data between fast and slow memory.¹² This approach enables handling large documents and maintaining long-term conversations without requiring all information to fit in the context window.¹²

Task-adaptive context windows

Future models may dynamically adjust their context window size based on the specific task requirements, optimizing the balance between comprehensive context and efficient processing.⁵¹³ This approach would allow models to expand or contract their attention span as needed.

Multimodal context integration

As models increasingly process multiple modalities (text, images, audio, video), context windows are evolving to handle these diverse inputs coherently, enabling more sophisticated multimodal reasoning and generation capabilities.⁵¹⁵

References

1. McKinsey & Company. (2024, December 5). "What is a context window?" Retrieved from mckinsey.com
2. Anthropic. (2024). "Understanding the context window." Retrieved from docs.anthropic.com
3. Hopsworks. (n.d.). "Context Window for LLMs." Retrieved from hopsworks.ai
4. Nebius. (2024, November 27). "What is a context window in AI? Understanding its importance in LLMs." Retrieved from nebius.com
5. Barkley, W. (2024, November 22). "The Prompt: What is long context — and why does it matter for your AI?" Google Cloud. Retrieved from cloud.google.com
6. Appen. (2024, April 11). "Understanding LLM Context Windows: Implications and Considerations for AI Applications." Retrieved from appen.com
7. IBM. (n.d.). "What's an LLM context window and why is it getting larger?" Retrieved from research.ibm.com
8. Anthropic. (2024). "The context window with extended thinking." Retrieved from docs.anthropic.com
9. Zilliz. (n.d.). "What is a Context Window in AI?" Retrieved from zilliz.com
10. Hopsworks. (n.d.). "Why is a large context window size important?" Retrieved from hopsworks.ai
11. Nebius. (2024, November 27). "The relationship between tokens and context windows." Retrieved from nebius.com
12. Dong, Z., Li, J., Men, X., et al. (2024). "Exploring Context Window of Large Language Models via Decomposed Positional Vectors." Retrieved from arxiv.org
13. Zilliz. (n.d.). "Challenges with Expanding Context Windows in AI Models." Retrieved from zilliz.com
14. Smith, M. S. (2024, September 16). "How 'Long Context' Improves Chatbots' Attention Spans." IEEE Spectrum. Retrieved from spectrum.ieee.org
15. Meta. (2025, April). "Llama 4: A collection of pretrained and instruction-tuned mixture-of-experts (MoE) large language models." Retrieved from llama-4.md