What Is an AI Context Window?

Key takeaways

The context window of an artificial intelligence (AI) model measures how much information the model can remember.

• A larger context window allows AI models to summarize information and give more accurate, complex, and nuanced responses to your prompts. 

• Context window sizes vary depending on whether you’re using Google Gemini, Anthropic Claude, or OpenAI ChatGPT.

• You can use context windows in your work as an AI researcher, data scientist, or AI developer. 

Learn more about how AI models use context windows to provide more accurate and nuanced responses, and how to measure and compare the context windows of some popular AI models. If you’re ready to start learning about AI tools from a business perspective, enroll in the AI For Business Specialization. You’ll have the opportunity to learn how to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning in as little as four weeks. Upon completion, you’ll have earned a career certificate for your resume.

What is a context window in AI?

If an AI model thinks similarly to how a human thinks, then a context window is the AI’s working memory, allowing the model to store information temporarily to use for the task at hand. When working with an AI model, a context window is the amount of information you can give the machine to get an accurate response to your prompt. For example, you could ask an AI model to read a news article and summarize the main points. The AI model would need to be able to remember your prompt, all of the news article’s content, and its response to deliver a cohesive answer to your request. 

This is a light job for many of the AI models available in the market today. With a large enough context window, you could ask an AI model to summarize a whole book, a series of books, or even a library. Beyond summarizing, larger context windows allow AI models to give more accurate, complex, and nuanced responses to your prompts. 

What can a larger context window do?

If you’ve ever worked with a large language model, you may have noticed that the model can forget things you discussed earlier within a long conversation. This can be frustrating when you are working on a longer project. A larger context window means that the AI can keep more information while you are working, allowing you to ask for more complex prompts, analyze longer texts, perform multi-prompt tasks, and more. 

With larger context windows, researchers have shown that AI can complete complex, nuanced tasks. For example, Google researchers gave their Google Gemini 1.5 Pro model Kalamang as context for a translation task, which has a single grammar manual still in existence for a critically endangered language. For context, this language has fewer than 200 speakers, and the grammar book was compiled between 2015 and 2019 in an attempt to document Kalamang [1]. Although the model did not have prior training in translating from English to Kalamang, it could perform translation tasks at a similar skill level as a human performing the same task with the same information, using the grammar book as context [2].

Magic AI recently announced they’ve trained their still-in-development AI model on a 100-million-token context window. For perspective, that would equal roughly 750 novels or roughly 10 million lines of code [3]. While the applications of an AI with these capabilities will be understood more when the model is widely available, Magic AI is focusing its development on creating an AI that specializes in generating code with your existing code, documentation, and programming libraries. This includes libraries not available publicly online. 

What is the difference between a token and a context window?

It’s easier to understand how revolutionary a concept like a 100-million-token context window is if you first understand how to measure a context window and what a token is. Tokens are processing units that equal a character, a part of a word, or a short phrase. It’s tempting to use a measurement like word count to measure context windows, but turning language into tokens offers a significant advantage: It takes less computational power for an AI model to understand a series of tokens than words, which means this is a more efficient method for large language models. 

Despite the variable length of characters any given token can have, you can do some quick math to estimate how many tokens a text document will represent. You can assume that a document will have about 30 percent more tokens than words. However, it’s worth repeating that the number could be much smaller or larger depending on the type of document you have and the tokenization process you use. 

Read more: Tokenization in NLP: What Is It?

Context windows of popular generative AI models

Now that you have a better understanding of how researchers measure context windows with tokens and why context windows are essential, you may be curious to know how your go-to AI model compares to its peers. Explore how context windows differ between Gemini, Claude, and ChatGPT, then compare against the claims of Magic AI’s development team. 

• Magic AI (Still in development): 100 million tokens [3]

• Google Gemini 3.1: One million input tokens, 64,000 output tokens [4]

• Anthropic Claude Pro: 200,000 tokens [5]

• OpenAI ChatGPT-5: 400,000 tokens [6]

As you can see, increasing the context window of large language models to 100 million tokens would increase AI models' computational power and reasoning ability by over 10 times the current technology. 

How many words is a 1M context window?

One million tokens equals about 750,000 words, which is equivalent to 10 to 15 full-length novels.

Who uses context windows?

If you want a career working directly with context windows to create generative AI content, or if you’re looking for a career in increasing the power of AI technology and the context windows of AI models, you might consider becoming an AI researcher, data scientist, or AI developer. Consider these roles, including their average salary and job outlook in the United States. 

AI researcher

Average base salary in the US: $102,000 [7]

Job outlook (projected growth from 2024 to 2034): 20 percent [8]

As an AI researcher, you will work on projects that advance AI capabilities, such as finding ways to increase the context windows of AI models or working on projects that use AI tech to solve real-world problems. You will work with a team to conduct research, report your findings to stakeholders, publish results, and push the limits of this technology to new heights. 

Data scientist 

Average base salary in the US: $119,000 [9]

Job outlook (projected growth from 2024 to 2034): 34 percent [10]

As a data scientist, you will work with your company or organization to find meaningful insights from data. You will determine what data you need to answer questions, collect data, store and clean data, and then interact with and analyze the data to find patterns. In this role, you will report your findings to senior stakeholders and recommend how they can use your insight in actionable ways. 

AI developer

Average base salary in the US: $121,000 [11]

Job outlook (projected growth from 2024 to 2034): 15 percent [12] 

As an AI developer, you will work to create software applications using AI technology. In this role, you may work with a team of developers to design and create AI solutions. You will implement these solutions and train team members on how to use your software. 

Find the tools you need to keep moving forward 

Explore career paths, assess your skills, and connect with resume guidance while browsing our Career Resources Hub. Then, check out the following resources to learn more about AI:

• Learn from an expert: The AI Advantage: 9 Questions with UC Davis AI Instructor Sadie St. Lawrence 

• Explore AI: Open for a Primer on AI, ChatGPT and More 

• Study key terms: Artificial Intelligence (AI) Terms & Definitions 

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