Why in news?
The rapid evolution of large language models (LLMs) has brought attention to their “context windows” – the amount of text a model can process at once. In 2025–26 new models such as GPT‑4 Turbo and the Claude 3 family expanded context windows from tens of thousands to hundreds of thousands of tokens, sparking debate over their benefits and limitations.
Background
A context window is essentially a model’s working memory. It defines the maximum number of tokens (roughly chunks of text, each about four characters) that the model can consider when generating a response. Both the user’s input and the model’s output occupy this space. If the combined input and output exceed the limit, the model may truncate earlier content, refuse to continue or produce incoherent results. Different models offer varying window sizes: GPT‑4 has 8 k and 32 k tokens; GPT‑4 Turbo extends to 128 k; Claude 3 models offer around 200 k tokens; and experimental systems like Google’s Gemini have reached 1–2 million tokens.
Key points
- Tokens versus words: Tokens are not the same as words. The same context window can hold different amounts of text depending on language and complexity. For example, a 100 k‑token window might fit 75 000 words of prose but fewer lines of code because programming syntax uses more tokens.
- More isn’t always better: Bigger context windows allow longer documents to be processed in one go, enabling tasks like summarising books or analysing entire codebases. However, as the context grows, models can become confused, forget earlier details or hallucinate facts. Attention mechanisms scale quadratically with window length, making long contexts computationally expensive.
- Model differences: GPT‑4 Turbo’s 128 k‑token window supports roughly 96 k words; Claude 3’s 200 k‑token window goes further, while experimental Gemini models handle up to 2 million tokens, enough for textbooks or large datasets. Each model requires careful prompt design to make effective use of its capacity.
- Task design: For complex tasks, it may be more effective to split content into chunks and use retrieval‑augmented generation (RAG) to recall relevant pieces, rather than relying solely on a large context window.
Significance
- Choosing the right model: Understanding context windows helps developers select models that fit their needs, balancing memory capacity with cost and performance.
- Better prompts: Awareness of token limits encourages concise prompting and the use of summaries or external memory systems to manage long documents.
- Research directions: Improving long‑context models requires advances in attention mechanisms, memory architectures and evaluation benchmarks that test reasoning across tens of thousands of tokens.
Source: The Hindu
