Beyond Tokens: The Promise and Pitfalls of Long-Context Language Models

Introduction

Large Language Models (LLMs) have rapidly expanded their context lengths(the amount of text they can process in one prompt). Early GPT-3 variants were limited to ~2K-4K tokens, but recent models like OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini boast context windows ranging from 32K up to 2 million tokens​. These longer contexts promise the ability to digest whole documents, books, or even codebases in a single go, potentially transforming tasks like long document question answering, legal analysis, and more.

However, with increased context come with questions: Do these large windows truly have practical benefits? Or do issues like diminishing returns, higher costs, and the “lost-in-the-middle” problem offset their advantages? This blog provides a comprehensive analysis of large context lengths in LLMs – examining applications and effectiveness with real-world examples. Also a comparison of large-context approaches with retrieval-augmented methods, highlighting trade-offs in latency, accuracy, memory, and complexity is discussed.

Real-World Applications of Large Context Windows

Large context LLMs are being applied in diverse domains. By handling tens or even hundreds of thousands of tokens at once, they aim to solve tasks that smaller-context or retrieval-based models struggle with.

Long Document QA and Summarisation

One of the most well-known use cases is question answering (QA) and summarisation over very long documents. Anthropic’s showcased that with a 100K context window (about 75,000 words), it's possible to ingest entire novels or lengthy reports. For example, Claude was able to read The Great Gatsby (≈72K tokens) and spot a single modified line in just 22 seconds.

In summarisation, large-context models can produce comprehensive summaries of books or long reports in one shot. Henry Zhang’s experiment with Claude-100K involved feeding in Hemingway’s The Old Man and the Sea (26K words) to get a summary and even a poem inspired by it. The model could maintain continuity and capture details across the entire text. In enterprise settings, this translates to summarising financial reports or legal documents without chunking. AssemblyAI demonstrated transcribing a 6-hour podcast (≈58K words) and then using Claude to summarise it – a task that requires speech-to-text and long-context summarisation. Google’s Gemini 1.5 Pro demonstrated context window support up to 2 million tokens, and has been used to analyze entire books or multi-week chat logs in a single pass. Early use cases include long financial spreadsheet reasoning and literature review synthesis — though effective attention tends to flatten out beyond 400K tokens.

Codebase Understanding and Technical Documentation

Developers often face the challenge of understanding a large codebase or extensive documentation. With large context LLMs, one can literally “drop an entire codebase into the context and intelligently build on or modify it”. This enables queries like “Find where in this 100-page API spec the function X is defined and summarize its usage.” Models like Claude (100K) or GPT-4 128K can handle hundreds of pages of developer docs or code, making them potentially powerful coding copilots for big projects.

Legal and Financial Analysis

Legal contracts, legislative bills, and annual financial statements often span hundreds of pages. Large context LLMs allow analysts to input an entire contract or a year’s worth of filings and ask complex questions that require reasoning across the document. Anthropic specifically highlighted use cases such as: analysing strategic risks in annual reports, assessing pros/cons of legislation, and identifying themes or arguments across legal documents. This means a lawyer could ask, “Across these 200 pages of case law, what are the key differences in how precedent X is applied?” and get an informed answer without manual skimming.

A case study by Jerry Liu tested Claude’s 100K window on Uber’s SEC 10-K filings. The model, when prompted appropriately, could synthesise insights across the entire filing “to answer the question at hand”. It showed holistic understanding (covering multiple sections of the document) if guided with a good prompt strategy (like a tree-summarisation approach). The latency for processing a full 10-K (~60K tokens) was about 60–90 seconds​ – which, while non-trivial, was faster than making many separate calls with smaller contexts to cover the same material. This suggests large contexts can be time-efficient for single-shot complex queries.

Multi-Document Synthesis vs. Retrieval-Augmented Generation (RAG)

Perhaps the most ambitious use case is replacing explicit retrieval with a large context window – essentially doing multi-document synthesis in-context. If an LLM can take an entire corpus or multiple documents directly, one might avoid building a vector search index or splitting text into chunks. For example, instead of retrieving the top 5 articles about a topic, you could insert all relevant articles into the prompt (up to the context limit) and ask the model to synthesise an answer.

Anthropic hints that for complex questions, directly using a large context may work *“substantially better than vector search–based approaches”*​. The rationale is that vector search might miss subtle relevance or require chunking that loses context continuity, whereas a single prompt retains full text fidelity. Real-world scenarios include feeding multiple research papers on a topic into Claude and getting a nuanced summary that cross-references them, or providing a full conversation history (like days of customer support logs) to get a model’s analysis of customer sentiment trends.

However, fully substituting RAG with long context has limits (discussed next). It works best when the documents fit in the window and the model can truly attend to all parts. Examples like Claude’s 100K reading of “Great Gatsby” to find a single line edit show success in a controlled test. Yet, as context sizes grow, new challenges emerge in ensuring the model doesn’t “forget” the middle or get overwhelmed by irrelevant information.

Effectiveness of Large Context vs. Retrieval

This section evaluates whether large context windows deliver on their promise by combining findings from research papers, benchmarks, and anecdotal reports to identify where larger context helps—and where a smarter approach (like retrieval) might still be needed.

Haystack “Needle” Tests: A known evaluation is the “needle-in-a-haystack” test – burying a key fact in a long input and seeing if the LLM can find it. Early versions of this test showed that GPT-4 (8K and 16K) could recall buried info quite well, but as context stretched to 32K or more, performance dropped (GPT-4’s 64K performance was roughly on par with GPT-3.5 at 8K). A detailed DEV Community analysis expanded this: GPT-4 was “flawless on 8k” and strong up to 16k, but somewhat worse at 32k+. Meanwhile, Anthropic’s Claude (100K) excelled in tests purely due to having more context to search, often winning on recall when GPT-4’s limit was hit. The conclusion was that Claude 2 (100K) is the winner on context recall, followed by GPT-4, but when both operate in a regime well below their limits, they perform similarly. Updated benchmarks like Needle-in-the-Haystack 2.0 and RULER v2 confirmed that while GPT-4-128K and Claude 3 can maintain strong recall at 100K–200K, most models still degrade sharply past 32K unless specifically trained for long-range stability.

RAG vs. Long Context in Production: Databricks ran over 2,000 experiments on various models to compare RAG pipelines as context size varied. They found that retrieving more text helps up to a point – more relevant info increases answer accuracy – but beyond a certain context size, model performance often decreases. For instance, Llama 3.1 (405B parameters) started degrading after ~32K tokens context, and GPT-4 (0125-preview) after ~64K. Only a few models could maintain performance as context grew extremely large. This is likely due to attention dilution or hitting the model’s “effective” context limit. Another finding was that models fail long-context tasks in different ways: some refuse (thinking there might be copyrighted text), others always summarise instead of specifically answering, etc., indicating that training on ultra-long context is still insufficient.

Claude’s 100K Use Cases: The LlamaIndex blog on Claude-100K’s performance with SEC filings provides nuanced insight. Claude could synthesize across a 100K context when prompted with a structured approach (they used a “list index” of text, and then a tree_summarize response mode). The outcome: Claude gave coherent answers that drew from multiple parts of the filing, demonstrating a real ability to use the whole context. However, it missed some details – suggesting recall isn’t perfect – and struggled with a more complex “refine” prompt that required iterative reasoning. So while the breadth of context use was excellent, the depth of reasoning on that context had limits (especially for complicated instructions).

Story Generation: MPT-7B-Storywriter (65K+ context) is a fine-tuned model for fiction. Anecdotally, having a long context here means the model can refer back to events or details from much earlier in a story, maintaining consistency over tens of thousands of tokens. Users have reported that the quality of long-form story generation improved when the context window was extended, as the model could “remember” the beginning of the story while writing the end. This is a more qualitative domain, but it’s one where long context aids coherence and reduces repetition.

Challenges: “Lost-in-the-Middle” and Effective Context Limits

Two key issues often arise as we push context lengths higher:

• “Lost in the Middle” Problem: This phenomenon, identified in a 2023 paper (arXiv:2307.03172), notes that models often pay less attention to the middle parts of a long context compared to the beginning or end. Intuitively, if you give an LLM 100 pages, it might recall the first and last few pages well (recency and primacy effects), but things in the middle may be “lost”. Techniques like position embeddings extrapolation and training strategies try to mitigate this, but it remains a challenge. Recent research proposes methods such as MoICE (Mixture of In-Context Experts) to address this, by dynamically routing attention to different context segments so that middle tokens get their fair share of focus​.

  MoICE specifically showed improved long-context understanding by preventing the model from overlooking middle content, using multiple rotary position embedding (RoPE) “experts” per attention head. Claude 3, released in March 2025, showed significant improvements in mitigating the “lost-in-the-middle” effect, aided by innovations like MoICE, which uses per-head expert routing to balance attention across segments.

• Effective Context Length vs. Maximum: The RULER benchmark (arXiv:2404.06654) was designed to measure the real usable context length of models. It found that many LLMs have an effective context length much shorter than the advertised max window. For example, a model may have a 32K token window but beyond, say, 8K or 16K, its performance on tasks like retrieval or QA starts dropping sharply. This aligns with the Databricks findings where model quality dipped past certain thresholds. It underscores that just scaling the context in the architecture isn’t enough – models need training or fine-tuning to actually use that context effectively. If not, the extra tokens are largely wasted or even detrimental (noise that confuses the model). Fine-tuning strategies (like how MPT-7B-Storywriter was trained on 65K sequences) and positional embeddings like ALiBi or RoPE extrapolation aim to bridge this gap by exposing models to longer sequences during training, thus raising the effective context length. LongRoPE2, introduced in 2025, enables near-lossless scaling of position encodings, helping LLMs maintain high accuracy across 256K+ token windows — a major step beyond prior ALiBi or RoPE extrapolations.

When Larger Context Outperforms Retrieval (and Vice Versa)

When Large Context Wins: If your task requires reading a lot of contiguous text where contextual continuity matters, a large context LLM can shine. Examples include reading a long narrative, analysing a lengthy legal argument (where section A and section F need to be considered together), or summarising a big document. Large context is also beneficial when you don’t know what’s relevant – so you feed everything in. As noted, for “complex questions” spanning many parts of a text, directly using the whole text can beat a retrieval approach that might miss some connections. Also, from a simplicity standpoint, using one big prompt is easier to implement than maintaining a vector DB and doing multiple retrieve-then-read steps.

When Retrieval or Smaller Context Wins: If the needed info is sparse (e.g., one paragraph from a 300-page book), retrieval is far more efficient. Why feed 300 pages and pay for 300 pages of tokens, when a good retriever+8K model can find the one paragraph and feed just that? The Databricks study even posed the question: “Why retrieve individual documents… if you can insert the entire corpus into the context window?” – and their answer was that, beyond a point, it’s wasteful and less effective.

Retrieval-augmented generation (RAG) also allows updating knowledge without retraining the model or worrying about context window limits. For instance, a 128K LLM might handle your entire company wiki today, but next year if the wiki doubles, you’re stuck – whereas a retriever + small context can scale with your data growth.

Hybrid is common: A practical pattern is retrieval for broadness and large context for detail. You might retrieve 10 relevant documents via a vector search, which totals, say, 20K tokens, and then use a 32K context LLM to read those all together and answer the question. This ensures you’re not missing anything (thanks to retrieval) and still leverage the cross-document reasoning (thanks to large context). If the LLM had only 4K context, you might retrieve just 2-3 documents and risk missing something, or have to summarise and merge iteratively.

In summary, large context models are not a silver bullet to replace retrieval in all cases, but they reduce dependence on retrieval for tasks that fit within their (effective) window. When the domain of discourse can be constrained to the context limit, they offer a more integrated and possibly higher-quality solution than chunk-and-retrieve. But when dealing with truly massive corpora or open-ended info needs, retrieval and other memory tools remain indispensable.

Trade-offs in Handling Large Context in LLMs

This table compares four common strategies for handling long-context tasks in language models. Each row highlights a key factor, and how each approach performs in that area:

• Dense Context Models: Powerful LLMs (like GPT-4, Claude) that process large amounts of text in a single prompt using full attention.

• Sparse/Linear Attention Models: Architectures optimised for efficiency with large inputs (e.g., Longformer, BigBird, MPT-Storywriter).

• Retrieval-Augmented Models (RAG): Combine a retriever (like a vector database) with a smaller LLM to dynamically fetch only the most relevant chunks.

• Chunk + Summarise: A lightweight method where long inputs are split into chunks, each summarised separately.

​In 2025, the landscape of LLMs has evolved significantly, particularly in their ability to process and reason over extended contexts. Recent advancements have addressed many of the challenges previously associated with long-context LLMs, leading to notable improvements in efficiency, reasoning capabilities, and practical applications.​

Advancements in Long-Context LLMs:

• Extended Context Windows: Models like Google's Gemini 2.5 Pro have expanded context windows to handle up to 2 million tokens, enabling the processing of entire books or comprehensive datasets in a single pass. ​

• Enhanced Reasoning Abilities: The integration of "reasoning" capabilities allows models to process tasks step-by-step, resulting in more accurate and informed responses to complex prompts. ​

• Efficient Processing Techniques: Innovations such as LongRoPE2(arXiv:2502.20082) have enabled near-lossless scaling of context windows, extending effective context lengths while preserving performance on shorter contexts. ​

While these advancements are promising, certain challenges remain:

• Computational Resources: Processing long contexts requires substantial computational power, though innovations like the "mixture of experts" method are being explored to optimise resource utilisation. ​

• Information Prioritisation: Ensuring that models effectively prioritise and retain critical information throughout lengthy inputs continues to be an area of active research.

In conclusion, the developments in 2025 have significantly enhanced the practicality and effectiveness of long-context LLMs. While challenges persist, ongoing research and technological innovations are continually improving their performance and expanding their range of applications.