How does Context Window Size Affect Prompt Performance of LLMs? [Tested]

The context window of a language model refers to the amount of text it can process in a single prompt, and it plays a critical role in prompt performance. Larger windows often improve accuracy, reduce hallucinations, enhance coherence, and support long or multi-step tasks.

However, they also increase computational cost and latency. Smaller windows are faster and more efficient, which makes them suitable for short and simple prompts. It is important to understand that context window sizes vary across models.

Big models support up to 200,000 tokens, while others operate with more limited capacities. I tested some models with 2k – 100K token prompts to see how does context window size affect prompt performance of LLMs and have shared the results below.

What is a Context Window in LLMs?

A context window in a large language model acts like short-term memory. It defines how much text the model can process at once, including your prompt, earlier messages, and system instructions. The size is measured in tokens, which are small units of text.

For example, the sentence “I love AI tools!” uses about five tokens. If your input exceeds the model’s context window, it may lose track of earlier parts. Understanding this helps you write clearer and more effective prompts.

What are Input and Output Context Windows in LLMs?

Input and output context windows define how much information a language model can process and generate in a single interaction.

• Input context window refers to the total number of tokens the model can read at once. This includes your prompt, system instructions, and any previous messages.
• Output context window refers to how many tokens the model can generate in response. This is usually a subset of the total context limit.

Example: If a model has a 128K token limit and is given 100K tokens of input, it can only generate up to 28K tokens in output. Exceeding the input limit may result in the model ignoring or truncating earlier content.

Understanding both helps you manage prompt length and avoid loss of important information during long interactions.

Why Do Context Window Sizes Vary Between LLMs, and What Role Do They Play?

Not all language models process the same amount of text at once. The context window size determines how much information a model can “remember” while responding.

Some models are built for short, focused tasks, while others are designed to handle entire documents or multi-turn conversations. The bigger the window, the more text the model can process in one go, but it comes with tradeoffs in speed and cost.

Here is a quick glance at the token limits of different LLMs:

Why Does Context Size Matter So Much?

• A larger context window allows a language model to process more information at once, which can significantly impact the quality of its responses. It improves how the model handles complex instructions, multi-step reasoning, and long conversations, reducing LLM hallucinations.
• Longer Instructions & More Context: With a large context window, you don’t have to choose between instructions and data. You can include both, improving answer quality.
• Maintaining Continuity: In multi-turn chats or research sessions, large windows let the model keep track of past queries and your tone, reducing repetition.
• Fallback When Retrieval Fails: Retrieval-augmented systems (like RAG pipelines) are great but if they miss key information, models with large native context windows offer a backup layer of memory. This becomes even more useful when handling Query Fan-out, where one prompt spawns multiple sub-queries requiring deeper context retention.

How does Context Window Size Affect Prompt Performance of LLMs? [My Testing]

As context windows grow larger, it’s natural to expect better performance. But does a bigger window always mean better results?

At AllAboutAI.com, I tested top LLMs at both short and long context sizes to evaluate how they handle different prompt lengths across reasoning, question answering, and instruction-following tasks.

Models Tested

• GPT-4o (128K tokens)
• Claude 4 Sonnet, originally tested here with a 200K token context window, now supports a 1M token context window which matches GPT 4.1 and Gemini 2.5 Pro in long document handling.
• Gemini 2.5 Flash (1M tokens)

Test Setup

Each model was prompted with:

• A reasoning task (with key info at start, middle, and end)
• A summarization task over a long document
• A Q&A task requiring recall across sections

Each model was tested on three tasks, each at two context lengths:

• Short (~2K tokens)
• Long (~200k tokens)

1. Reasoning Test

2. Mid-Document Q&A Test

3. Instruction-Following Test

Results Summary: Prompt Performance Across Context Sizes

After running the three test prompts across ChatGPT-4, Claude 4 Sonnet, and Gemini 2.5, here’s how each model performed at different context lengths and task types:

How Larger Context Windows Influence Model Behavior? [Key Factors Explained]

Larger context windows significantly affect how language models understand and respond to prompts. As token capacity increases, models can process more of the input in a single pass, improving comprehension, but also introducing new trade-offs. Here’s how larger windows influence model behavior:

• Increased Contextual Understanding: With a larger context window, models can consider more of the input text, including previous dialogue, documents, or instructions. This enables more nuanced and context-aware responses by allowing the model to retain and reference a broader range of information.
• Improved Coherence and Accuracy: A wider context helps the model maintain logical flow and factual consistency in its outputs, especially during long-form generation. It also helps reduce hallucinations by grounding responses in a larger set of input data.
• Better Handling of Long Prompts and Tasks: Larger windows allow models to process and understand complex or multi-part prompts in one go. This is especially valuable in scenarios like document summarization, code analysis, or multi-turn conversations where continuity is essential.
• Increased Computational Cost and Risk of Irrelevant Information: Processing more tokens requires more compute power and memory, leading to slower response times and higher costs. Additionally, the model may inadvertently attend to less relevant or outdated information within a large window, potentially lowering output quality.
• Added Complexity in Prompt Engineering: As context length grows, crafting effective prompts becomes more challenging. You need to structure inputs carefully to ensure the most important details remain accessible and are not drowned out by less relevant content.
• Trade-offs Between Size and Performance: While more context improves capability, it does not always yield better performance. Sometimes, too much information can dilute attention or introduce ambiguity, making balance and prompt design critical.

What is Thomson Reuters’ Findings on Effective Context Windows?

Thomson Reuters benchmarked large language models (LLMs) to assess how well they handle long-context tasks in high-stakes fields like law, compliance, and finance.

While many models now support context windows of 100K+ tokens, their research found that the effective context, the portion a model can reliably reason over, is often much smaller.

In practice, this means models may struggle with nuanced reasoning even when provided with the full input, especially in legal scenarios.

What are the Recent Benchmarks & Studies on Context Windows?

These insights emphasize that while expanding context windows offers potential, it also introduces challenges that require careful consideration in model design and prompt engineering.

Real-World Example: Reddit Users Compare Claude and ChatGPT Context Handling

In a detailed Reddit thread, users tested how ChatGPT Plus and Claude handle long documents, like the full text of Alice in Wonderland (~30K words). The experiment involved inserting specific errors into the text and asking both models to detect them.

The results revealed a key difference: ChatGPT Plus uses retrieval-augmented generation (RAG) for uploaded files, meaning it breaks the document into chunks and retrieves only the ones most semantically related to your question.

If your prompt doesn’t contain the right keywords, it may miss critical content. In contrast, Claude Sonnet processed the entire document natively, thanks to its 200K token context window, and identified all inserted errors accurately.

Its context capacity has since grown to 1M tokens which enables it to handle even longer documents without retrieval splits.

This showcases the practical importance of true context window capacity versus background retrieval. While API access may unlock ChatGPT’s full context, users on the Plus plan are effectively limited to 32K tokens, something that affects comprehension in subtle but significant ways.

Redditors concluded that Claude and Gemini are more reliable for long-document tasks, while ChatGPT remains strong for general queries and speed, especially when context depth isn’t critical.

How Have Recent Models Expanded Their Context Windows Over Time?

Over the past few years, LLMs have dramatically increased their context window sizes, enabling them to process and retain more information in a single interaction. This evolution has unlocked new capabilities, such as handling entire books, extensive codebases, and prolonged conversations.

Do Context Windows Matter for AI Agents?

AI agents often perform multi-step tasks, maintain working memory, and process sequences of evolving instructions. A larger context window allows an agent to track more past actions, inputs, and goals without needing to “remind” it at every step.

When the window is too small, agents may lose task continuity, miss prior steps, or repeat actions. This is why selecting an LLM with a large enough context window is crucial for agent-based workflows, such as multi-hop reasoning, long conversations, or reading multiple documents in a chain.

Context Window Issues: Why LLMs Forget Your Prompts?

Language models don’t have infinite memory. Their ability to process information is limited by a context window, which defines how many tokens they can “see” at once. When this window is exceeded, important content can be silently dropped.

But surprisingly, forgetting can also happen within the window due to how attention is distributed.

Here are some reasons why LLMs forget:

• Token Limit Overflows: If your prompt exceeds the context window (e.g., 4K for GPT-3.5), the earliest tokens are truncated. This happens quietly. No error, just loss of context. OpenAI API documentation notes this behavior explicitly.
• Mid-Prompt Blind Spots: Transformers often assign higher attention to start and end tokens. Tokens in the middle can get less focus, leading to “center drop.”
• Prompt Dilution: Large, unfocused prompts can scatter attention and reduce answer accuracy. Important instructions may be buried under less relevant tokens.
• Model-Specific Handling: Some models (like Claude 3 or Gemini 1.5) use smart techniques like sliding windows or retrieval augmentation to reduce forgetting. Others (like GPT-3.5) simply clip earlier tokens without adjusting for relevance.

Why is the Context Window Considered an AI’s Working Memory?

In humans, working memory refers to the ability to hold and manipulate information temporarily, like remembering a phone number just long enough to dial it. For AI models, the context window plays a similar role.

It’s the space where the model “remembers” all the information it’s actively using to generate a response, including your prompt, system instructions, and previous conversation turns. Unlike human memory, however, this context has strict size limits, measured in tokens.

If your input stays within the context window, the model can reason about everything fluidly. But once you exceed that limit, the earlier tokens are dropped, just like forgetting what someone said at the start of a long sentence.

That’s why the context window is often described as the AI’s “short-term memory”. It’s what the model can hold in mind right now, without needing external tools like databases or memory modules.

What are the Architectural Innovations That Enable Long-Context Handling?

As context windows have grown, language model architects have had to rethink how models manage such large inputs efficiently.

Traditional transformer models face performance and memory bottlenecks as input size increases, so researchers have developed new strategies to help models handle long contexts more intelligently without overwhelming compute resources.

• Rotary Position Embeddings (RoPE): Used in models like GPT-4 and Claude, RoPE encodes the position of each token in a way that scales more smoothly across longer sequences.
• ALiBi (Attention with Linear Biases): Prioritizes nearby tokens while still allowing attention to longer-range content. Helps maintain efficiency without losing contextual relevance.
• FlashAttention: An optimized attention mechanism that reduces memory use and speeds up computation, critical when processing 100K+ tokens.
• Chunked & Sliding Window Attention: Breaks long sequences into manageable segments while allowing partial overlap, preserving coherence across large documents.
• Memory Compression and Summarization: Some models use internal summarization techniques or “compress past” strategies to retain essential information while freeing up space.
• Sparse Attention & Longformer-style Approaches: Not all tokens need to attend to everything. Sparse models focus only on the most relevant token relationships, cutting down on unnecessary computation.
• Retrieval-Augmented Techniques (RAG): Rather than feeding all data into the context window, these models fetch relevant chunks from an external database or vector store. Combines memory and logic efficiently.

These innovations are what make models like Claude 4, Gemini 2.5, and GPT-4.1 capable of reasoning over entire books, conversations, or codebases without losing their footing. Without them, scaling context windows would come at an unsustainable cost.

What are the Tradeoffs of Expanding a Language Model’s Context Window?

Increasing the context window in a language model sounds like a no-brainer: more memory, better results, right? In reality, it’s a tradeoff. While larger windows allow for deeper analysis and more flexible prompting, they come with notable downsides in performance, efficiency, and accuracy.

When to Use Large Context and When Not To?

Not every task needs a massive context window. Knowing when to use it (and when not to) can save time, cost, and confusion. Below is a quick comparison to help guide your prompting decisions:

What are the Best Practices for Prompting Across Context Sizes?

Prompting effectively means adapting your strategy to the context window size. Whether you’re working with a short-form model like GPT-4 or a long-context giant like Claude 4 or Gemini 2.5, the way you structure your input makes a big difference.

✅ For Small Context Windows (up to 4K–8K tokens)

• Be concise and direct. Use compact instructions with clear intent.
• Avoid unnecessary background. Every token counts. Include only the most relevant data.
• Use system prompts wisely. In models like GPT-3.5, system messages take up space. Keep them tight.

✅ For Medium Context Windows (8K–32K tokens)

• Add structured examples. You can now include few-shot examples, task formats, or schema.
• Maintain logical flow. Help the model follow your intent by organizing content into steps or bullet points.
• Repeat key instructions at the end. Reinforce core commands to compensate for potential mid-prompt fading.

✅ For Large Context Windows (32K–1M tokens)

• Use chunked documents. If feeding long inputs (e.g., PDFs), break them into thematic sections.
• Place critical content at the beginning and end. Models often focus more on edges of the input.
• Use retrieval-based filtering. Combine with vector search (e.g., LangChain, LlamaIndex) to reduce noise.
• Consider summarization. Pre-summarize or abstract long sections before adding to the prompt.

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FAQs – How does Context Window Size Affect Prompt Performance of LLMs?

Final Thoughts

In summary, the context window size plays a pivotal role in shaping the effectiveness of large language models across tasks. While longer windows unlock powerful capabilities for processing complex inputs, they also introduce trade-offs in speed, cost, and accuracy.

Strategic prompting, not just token count, remains essential for optimal results. So, how does context window size affect prompt performance of LLMs? It depends on the task, the model, and how you use them. Have you seen better results with longer prompts? Share your thoughts in the comments below!