Context window

Context window in large language models (LLMs) — is the maximum amount of textual information (in tokens) that a model can consider when generating a response. In other words, it is a kind of "working memory" of the model, determining how much text (including both the user's original query and the model's previously generated phrases) it can hold in context simultaneously. The context window size is measured in tokens — conditional units of text (words, their fragments, or characters) into which input is split for processing by the model. The coherence and relevance of generated responses directly depend on the context window length: a large context volume allows the model to better account for preceding information, retain details of prolonged dialogues, and not lose meaning when working with long documents.

Evolution of Context Window Sizes

The first transformer language models had a relatively small context window. For example, in 2018-2019, the maximum context length was around 512-1024 tokens. The GPT-3 model (2020) could already process up to 2048 tokens at once. At the beginning of ChatGPT's operation (2022), the context limit was about 4000 tokens (approximately 3000 words), which limited conversation length – when exceeding ~3000 words, the chatbot would start to "get lost" and hallucinate off-topic.

Modern flagship models have significantly increased this threshold: GPT-4 is available in versions with windows of 8192 and 32,768 tokens, and Anthropic's Claude model received a window of 100,000 tokens in 2023 (approximately 75 thousand words, or several hundred pages of text). By 2024, models appeared with context around 128 thousand tokens (e.g., LLaMA 3.1 from Meta) and even up to 1 million tokens (Google Gemini 1.5 Pro). In 2025, LLAMA 4 Scout was announced with a record context window of up to 10 million tokens, equivalent to text volume of tens of thousands of pages. However, such extreme values are largely theoretical: memory limitations and training data do not allow the model to fully utilize the entire 10-million context in practice. Nevertheless, the race to increase the context window has become a new stage of LLM development, comparable in significance to the growth in the number of model parameters.

Below are examples of maximum context length for several models:

• GPT-3 – up to ~2048 tokens
• GPT-4 – 8192 tokens (standard version) and up to 32,768 in extended version
• Anthropic Claude – up to 100,000 tokens
• LLaMA 3.1 – up to 128,000 tokens
• Google Gemini 1.5 Pro – up to 1,000,000 tokens
• Meta LLAMA 4 Scout – claimed up to 10,000,000 tokens

Context window growth radically expands model capabilities. If 32 thousand tokens correspond to approximately 50 pages of text, then 100 thousand tokens is about 75 thousand words. A model can process such volume in seconds, for example, analyzing an entire novel or technical report and identifying needed details. Thus, models with long context can hold entire books, large document sets, or long dialogues in memory, opening new application scenarios — from detailed summarization and cross-document question-answering analysis to working with large source code fragments.

Limitations and Problems of Long Context

Increasing the context window is associated with serious technical and practical challenges. The main one is combinatorial growth of computational complexity. In transformers, the self-attention mechanism has quadratic complexity with respect to sequence length: when doubling the context length, the required memory and computation volume increases approximately fourfold. For example, transitioning from a context of 1024 tokens to 4096 tokens theoretically increases resource costs ~16 times. This imposes limitations both on the training stage (where overly long sequences are difficult to use due to GPU memory limitations and training time) and on the model application stage - long queries significantly slow down response generation and increase its cost when using commercial APIs. Processing input tokens usually incurs charges, so long texts fed to the model proportionally increase response cost.

Information overload is another important factor. Although a large window allows feeding more data to the model, excess details can lead to the model failing to identify the main points among the "noise". Research shows that modern LLMs perceive relevant information unevenly: they tend to pay more attention to facts placed at the beginning or end of long context input (primacy and recency effects) and extract knowledge from the middle of a large document much worse. Saturating the prompt with unnecessary details can reduce answer accuracy. Thus, beyond a certain limit, increasing context volume can be counterproductive. A practical consequence of this is the recommendation to include only truly necessary data in long queries and structure the context so that key information is closer to the beginning (or end) of the message.

Additionally, in practice, a discrepancy was discovered between the nominal window length and what the model effectively uses. Many models cannot work equally well with the entire available length — their effective context depth is significantly less than the maximum. For example, the LLaMA 3.1 model with a trained context of 128k showed in tests that information located beyond ~64k tokens from the beginning practically did not affect responses. Overall, for most open LLMs, it is noted that their real effective memory is less than half of the intended context length. Researchers attribute this to training peculiarities: even if a model is formally trained on long sequences, extremely distant positions appear in data much less frequently than initial ones, causing the model to be undertrained at the end of the window. In typical corpora, the frequency of very long sequences decreases exponentially. Such "left-skewed" position distribution leads to the model learning near context significantly better than far context. Solutions may include more careful selection and annotation of training data, as well as special methods compensating for undertrained positions. Overall, overcoming this limitation is an active research area.

Methods for Extending Context Window

Extending the LLM context window requires a combination of architectural and algorithmic improvements. Main directions used in modern works include:

• Training on long sequences. An obvious approach is to provide the model with training examples comparable to the desired context length. Curriculum learning by length is practiced: gradually increasing text sizes during training. Techniques like gradient accumulation and special data preprocessing are also used.
• Attention mechanism optimization. Since standard self-attention has quadratic costs, alternatives are actively researched: sparse attention, sliding window, multidimensional context partitioning, etc.. For example, Ring Attention is an attention optimization method proposed by IBM that reduces computational load for long sequences. Adding ring attention to IBM's Granite model allowed significantly increasing the context.
• Improving positional encodings. A crucial part of the transformer is the method of encoding token positions. Classic absolute positional encoders extrapolate poorly beyond the length they were trained on. Therefore, relative positions and other methods are used for long context. For instance, the Granite model in its 128k context version switched from absolute position to relative position token encoding. Rotary positional encoding (RoPE) is widely used, which better preserves the relative positioning of distant tokens and allows context scaling. Another approach – Attention with Linear Biases (ALiBi) – introduces linearly increasing bias into the attention mechanism for large distances. Combining such techniques – for example, scaling the base frequency of RoPE (as implemented in LLaMA 3) – is now used to enable models to support windows of 100k+ tokens.
• Memory and context compression. An alternative path is not to directly increase window length, but to compactly represent long input. For example, one IBM technology involves the model generating a compressed representation (summary) of long text using another LLM. Another approach is connecting external long-term memory or knowledge bases: the model stores important facts outside its context window and loads them when needed. The latter option has developed into methods known as retrieval-augmented generation (RAG).

It is important to note that each of the listed strategies has its cost. Training on long contexts requires enormous computational resources and carefully selected data. New attention and position mechanisms complicate model architecture and sometimes reduce quality on short texts. Therefore, engineers must carefully balance between window size, training stability, and final model performance.

Large Contexts vs. Information Retrieval (RAG)

The growth of maximum context in LLMs to hundreds of thousands and more tokens has sparked debate about whether external knowledge bases and search algorithms are needed with such model capabilities. If all relevant information fits directly into the context window, the model can theoretically answer without accessing external sources. Some researchers suggest that with increasing windows, methods like retrieval-augmented generation (RAG), where the model receives pre-extracted texts from a database, may become obsolete. In favor of this, for example, are information losses at the retrieval stage: search returns only a few top documents, while "prompt stuffing" (direct inclusion of data in the query) allows feeding the model all contextual information at once. IBM researcher Pin-Yu Chen notes that no one will want to bother with RAG setup if they can simply load all needed books and documents into the model at once.

However, the opposite view is that even a very large window does not eliminate the need for RAG. IBM representatives and other experts emphasize that data timeliness and control remain a serious problem. A model with an enormous context still doesn't know what wasn't in its training data — for example, today's news. For promptly including fresh information on request, a retriever mechanism is necessary. Additionally, in enterprise applications, RAG allows selectively pulling facts from protected storage while observing access rights and not disclosing unnecessary confidential data. Finally, economic considerations are also important: processing millions of tokens "idly" is expensive, and it is often more reasonable to first find several truly relevant excerpts (reducing context) than to force the model to read thousand-page input each time. For these reasons, RAG remains an important component of AI applications for now, and large context windows are recommended to be used judiciously. Probably, hybrid approaches — combining extended context (for storing frequently used data as cache, Cache-Augmented Generation) and selective retrieval of new knowledge from external sources — will become the optimal architecture.

Applications and Perspectives

Increasing available context significantly expands the range of tasks solved by language models. Summarization and analysis of long documents is one of the immediate applications. A model with a 100k token window can read a voluminous report, book, or technical documentation in a single query and provide a summary or answers to questions about them. This finds application in law (parsing and summarizing contracts), science (automatic literature review), and business analytics. For example, Claude successfully processed the entire novel "The Great Gatsby" (~72,000 tokens) and could identify pinpoint edits in the text within seconds.

Support for prolonged dialogues. For chatbots, a large context means the ability to remember dozens and hundreds of utterances. An extended window also allows integrating extensive reference data into the conversation.

Programming and working with code. In tasks related to source code analysis, long context has proven especially valuable. Code is often distributed across multiple files; to give a correct answer, the model must "see" as large a fragment of the codebase as possible. IBM research has shown that context extension noticeably improves model quality on code generation tasks. The Granite model with a 128k token window can perceive large volumes of library documentation in a query.

Multimodal applications. The newest models (such as the already mentioned LLaMA 4, Gemini) are multimodal and can accept not only text but also other data types (audio, images, video) as input. Large context helps here, for example, to analyze long audio recordings (conversation transcripts) or video (frame sequences with descriptions) entirely. It is reported that the Gemini 1.5 model with a 1M token window can hold in context up to 1 hour of audio or 3 hours of video without losing important details. This opens perspectives for automatic transcription and summarization of multi-hour meetings, movies, etc..

Despite impressive achievements, experts emphasize that large context is not a panacea, but a tool requiring competent use. It significantly increases infrastructure requirements (memory, performance) and makes model deployment more expensive. Therefore, when developing LLM-based systems, it is recommended to carefully assess what context volume is actually needed for the task and combine approaches. Nevertheless, the trend is obvious: future models will strive to combine even longer context with its efficient use. Solving current problems (attention scaling, training on long sequences, eliminating "forgetting" of the middle) will allow next-generation LLMs to operate with even larger volumes of information while remaining accurate and consistent. This will significantly expand the boundaries of AI applicability — from a full-fledged assistant to complex analytical systems.

Links

• Why larger LLM context windows are all the rage - IBM Research
• Context Length in LLMs: What Is It and Why It Is Important - DataNorth
• Understanding the Impact of Increasing LLM Context Windows - Meibel
• Introducing 100K Context Windows - Anthropic
• Lost in the Middle: How Language Models Use Long Contexts (arXiv)
• Why Does the Effective Context Length of LLMs Fall Short? (arXiv)
• RAG in the Era of LLMs with 10 Million Token Context Windows - F5 Labs