It includes both what you type and what the AI says back. This “window” helps the model stay on track in a conversation or when writing something long.
For example, if ChatGPT has a context window of 100 tokens, it can only remember the last 100 pieces of text (words, spaces, punctuation) while replying. If your conversation goes beyond that limit, it may forget earlier parts of discussion.
The image shows how a language model like ChatGPT uses a context window to understand and generate text. It focuses on a target token (the current word) by looking at tokens to the left (past words) and sometimes tokens to the right (future words).
The number of tokens refers to the total amount of text the model can handle at once. Anything beyond this limit gets ignored.
How does the Context Window work?
Let’s break it down into simple steps that show how an AI builds up its memory during a conversation.
- Turn 1: The Conversation Begins: You send your first message. The model stores it in its context window. Then it replies, and that response is also added. At this point, the window holds one user message and one AI reply.
- Turn 2: Adding More to Memory: You ask a second question. Now the AI sees everything from before: your first message, its earlier response, and your new question. It replies again, and that second reply gets added to the context window too.
- Turn 3 and Beyond: Growing Context: Each new user message and AI response continues to stack on top of the last. The context window keeps growing linearly until it reaches the maximum token limit. Once full, older content may be dropped depending on how the model is set up.
What’s the Limit of a Context Window?
A context window is the amount of information an AI model can remember during a conversation. This includes your past messages (input), the model’s replies (output), and any new messages added during the chat.
For example, Claude 3 supports up to 200,000 tokens in a single chat, showing how much memory some models can handle.
Within this context window, there are two parts:
- Input tokens refer to everything you send to the model, including your earlier prompts and system instructions.
- Output tokens are the words or replies the model generates in response.
For instance, if a model has a 100,000-token context window and your input takes up 90,000 tokens, then the model only has 10,000 tokens left for its output.
Note: Claude is mentioned as an example because it has one of the largest context windows. However, all language models have their own token limits, which determine how much conversation they can handle at once.
What is a Token?
A token is typically a word or part of a word.
For example:
“I love pizza” = 3 tokens
→ The phrase “I love pizza” actually consists of 3 tokens when processed by common language models like GPT-2 or GPT-3 tokenizers. Each word is typically one token, and spaces are not counted as separate tokens.
“internationalization” = 1 token
→ This is a single, long word that the tokenizer reads as one whole token.
As context windows become more advanced, it’s important to understand the LLM market driving this innovation.
The LLM Market in 2025
- The global LLM market is projected to grow from $1,590 million in 2023 to $259,800 million by 2030. The CAGR for this period is expected to reach 79.80 percent.
- In North America, the market is forecasted to hit $105,545 million by 2030 with a CAGR of 72.17%.
- In 2023, the top five LLM developers captured about 88.22% of global revenue.
- By 2025, there will be an estimated 750 million applications using LLMs.
- 50% of all digital work is expected to be automated through LLM-based applications by 2025.
These figures show how fast the LLM industry is growing and why advanced context handling is becoming essential
How Large Are the Context Windows in Today’s Leading LLMs?
Context windows (how much text an AI can understand at once) have expanded dramatically over time. Below is a quick comparison of popular LLMs as of October 2024:
| Model / Family | Total Context Window | Input Limit | Output Limit | Notes |
| GPT-3.5 | 4,096 → 8,192 tokens | Up to 8,192 | ~2,048–4,096 | Turbo version increased the limit |
| GPT-4 | 8,192 → 32,768 tokens | Up to 32,768 | ~4,096 | Earlier versions had lower limits |
| GPT-4 Turbo | 128,000 tokens | Up to 128,000 | ~4,096 | Input and output share the 128K window |
| GPT-4o / 4o Mini | 128,000 tokens | Up to 128,000 | ~16,384 | Output cap varies by deployment |
| GPT-4.1 (April 2025) | 1,000,000 tokens | Up to 1,000,000 | ~8,192 | Major upgrade over GPT-4o |
| Claude 3.5 Sonnet | 200,000 tokens | Up to 200,000 | ~8,192 | Standard offering |
| Claude Enterprise Plan | 500,000 → 1,000,000 tokens | Up to 1M | Varies | Extended context available for enterprise users |
| Gemini 1.5 Flash | 1,000,000 tokens | Up to 1,000,000 | ~8,192 | High-capacity model |
| Gemini 1.5 Pro | 2,097,152 tokens | Up to 2,097,152 | ~8,192 | Largest context window available commercially |
| Mistral Large 2 | 128,000 tokens | Up to 128,000 | ~8,192 | Mistral AI’s flagship |
| Llama 1 | 2,048 tokens | Up to 2,048 | ~512 | Initial version |
| Llama 2 | 4,096 tokens | Up to 4,096 | ~1,024 | Doubled from Llama 1 |
| Llama 3 | ~8,000 tokens | Up to 8,000 | ~2,048 | Launched April 2024 |
| Llama 3.2 (3B/11B) | 128,000 tokens | Up to 128,000 | ~8,192 | Matches other leading models |
Claude 3.5 vs Gemini 1.5 Pro vs Perplexity Pro vs ChatGPT-4o: Which AI Fits Your Needs?
Not all AI models work the same, even if their context size looks similar. The real question is which one helps you work faster, smarter, and better? Here’s a quick guide to help you choose.
| If you need… | Go with… | Why? |
| Summarizing books or research papers | Claude 3.5 or Gemini 1.5 Pro | Claude handles logic well. Gemini works great with visuals like tables and charts. |
| Fact-checked web search with source links | Perplexity Pro | It pulls live web data and shows sources clearly. |
| Conversational memory and coding help | ChatGPT-4o | Remembers past chats and gives strong coding support. |
| Visual analysis (diagrams, PDFs, screenshots) | Claude or Gemini | Both can read visuals. Claude is logic-focused, and Gemini offers a smoother interface. |
What Are the Key Benefits of Long Context Windows?
As enterprise LLM use cases grow more complex, long context windows bring powerful advantages:
- More Input in One Go: They let models handle entire documents, long prompts, or multiple data sources without cutting anything out. This is perfect for legal reviews, patient histories, or financial reports.
- Better Memory Across Conversations: Models can stay consistent during long chats, leading to smoother performance in customer support, meeting notes, or case follow-ups.
- Smarter Problem Solving: With a wider view of information, models can understand complex links across different inputs. This is ideal for advanced reasoning tasks.
When Long Context Is Unnecessary or Overkill?
While long context windows offer clear advantages, they’re not always needed and sometimes may even be counterproductive:
- Short Queries, Simple Tasks: For basic tasks like writing short emails, answering trivia, or generating quick code snippets, a large context window doesn’t add value and can increase processing time.
- Higher Cost and Latency: Models with large context windows often require more compute power, which can mean slower responses and higher costs even when the extra capacity isn’t used.
- Risk of Irrelevant Recall: With too much context, a model may focus on or repeat less relevant information from earlier parts of the conversation, reducing accuracy or clarity.
In these cases, using a model with a smaller context window may actually be faster, more cost-effective, and just as accurate.
Does a longer context window slow down response time?
What happens if my prompt includes mostly images or PDFs?
Can too much context confuse the model?
How Well Does ChatGPT Handle a Real Example with a Long Input?
To find out, I ran a simple test using a real research abstract. Here is how I did it, step by step:
- Step 1: Selected real content: I picked a research paper abstract about Large Language Models. It had detailed language and real examples, making it a good test case.
- Step 2: Turned it into clean text: I removed all images and formatting and copied the text into a plain editor. This helped keep only the actual readable words that the model would count.
- Step 3: Counted the tokens: I used OpenAI’s token counter to check the size. The text came out to be 705 tokens and around 3,000 characters.
- Step 4: Asked ChatGPT to read and answer: I gave ChatGPT a prompt asking it to summarize the main points, list the models mentioned, and explain what challenges were talked about in the paper.
- Step 5: Looked at the results: ChatGPT gave a short summary in four points. It listed 14 model names correctly and explained clearly what the paper said about the problems researchers face.
I tested how does context window size affect prompt performance of LLMs and the results were revealing: models with larger context windows generally maintained coherence over longer prompts, but beyond a certain threshold, performance plateaued or even declined slightly.
What Are the Key Challenges of Using Long Context Windows in LLMs?
Extending the context window (the amount of text an AI can read and understand at once) brings several performance and security issues:
- Cognitive overload: Like humans, LLMs (Large Language Models) can get confused when given too much detail. They may miss key points or guess instead of reasoning.
- Middle-context blindness: Studies show LLMs work best when important info is at the start or end of the input. They often ignore or mishandle information placed in the middle of long texts.
- Attention decay over long distances: Even with improvements like RoPE (Rotary Position Embedding), which helps the model track the position of each word, models still struggle to connect distant parts of a long input.
- Retrieval inconsistency: Tools like NIAH (Needle-in-a-Haystack), RULER, and LongBench test if models can find relevant info in big texts. Results show they often fail when the context gets too long.
- Retrieval inconsistency: Tools like NIAH (Needle-in-a-Haystack), RULER, and LongBench test if models can find relevant info in big texts. Results show they often fail when the context gets too long.
NIAH (Needle-in-a-Haystack): A benchmark that checks if a model can retrieve a small, specific fact hidden within a large block of text.
RULER: A task designed to test a model’s ability to identify and extract long-range dependencies between facts scattered across a document.
LongBench: A comprehensive benchmark suite evaluating how well models handle long-context tasks like summarization, QA, and reasoning across extended text.
- Security risk increases: A longer context gives more space for adversarial prompts, which are sneaky text inputs designed to manipulate the model.
- Higher jailbreak risk: Research from Anthropic found that longer inputs make it easier for attackers to bypass safety rules and get the model to say harmful things.
Long context doesn’t guarantee smart support. As Kevin Lee, Chief Digital Officer at BT Group, puts it:
“All you want is not a gibbering chatbot to tell you a ‘step one, step two’, but rather ‘give me the right person, right away to understand’.”
What Happens When Extended Thinking Is Turned On?
When extended thinking is enabled, Claude creates internal “thoughts” before giving you a final reply. These are called thinking blocks and are meant to improve the quality of their responses. Here’s how it works:
- Claude adds your message, its reply, and its thinking block into the current turn.
- After that turn, the thinking block is automatically removed from memory by the system.
- This means it won’t take up space in future context, leaving more room for your actual conversation.
How Context Windows Are Evolving in 2025?
Context windows are getting much larger, making AI models more powerful and versatile. Top models now support up to 15 million tokens, with Google and Meta both working around the 10 million mark.
By the end of 2025, Anthropic and Microsoft are expected to release models with 100 million-token capacity.
These models are also improving in long-context reasoning, meaning they stay relevant across entire documents instead of just focusing on the beginning or end.
Plus, many are now multimodal, using text, images, and even video within the same context to generate smarter and more flexible responses.
Explore These AI Glossaries!
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FAQs
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Conclusion
A context window is the amount of text an AI can read, remember, and respond to in one conversation. It includes your inputs and the model’s replies, helping it stay consistent and relevant.
As context windows grow, AI becomes better at handling longer tasks, but knowing their limits still matters. If you’re unsure about any AI terms, check out our AI glossary.
Have questions or thoughts? Share them in the comments below!
