What the EdTech?
What the EdTech?
Understanding AI Context Windows in 2026
Use Left/Right to seek, Home/End to jump to start or end. Hold shift to jump forward or backward.
Hey everyone, if you're using AI tools like ChatGPT, Claude, Gemini, or Copilot, there's one concept you really need to understand, the context window. Think of it like this the context window is the AI's short-term memory. It's the total amount of text, your prompts, and the AI's responses that the model can hold during a single conversation. Once you exceed that window, the AI starts forgetting the earlier parts of your conversation. It doesn't tell you, it just quietly loses track. Why does this matter? Let's say you're working on a long document, a strategic plan, a board policy, a curriculum guide, and you're going back and forth with the AI. If your conversation exceeds the context window, the model might contradict itself or lose the instructions you gave it at the start. Understanding this helps you work smarter. Context is measured in tokens. A token is roughly three-quarters of a word. So a 200K token window is roughly 150,000 words, more than a full novel. Now let's look at where the four biggest models stand right now, and the differences are significant. Starting with Google's Gemini 3 Pro, it offers a 1 million token context window as standard, with enterprise configurations scaling up to 2 million tokens. That's massive. We're talking entire code bases, hundreds of pages of documents, even hours of video, all in a single conversation. Gemini 3 also introduced agentic capabilities that let it browse, execute code, and call tools directly within its reasoning loop. Next, OpenAI's GPT 5.2. This is now the default model in ChatGPT as of February 2026. Here's where it gets interesting. The context window depends on your plan. Free users get 16K tokens, plus and business get 32K, and pro and enterprise users get up to 128K tokens on the instant model. The thinking mode, where GPT 5.2 reasons more deeply, gives all paid users 196K tokens. Now for Anthropic's Claude Opus 4.6, their most powerful model, released just two weeks ago. It offers a 200k token standard context window, with a 1 million token context window available in beta. What makes Opus 4.6 stand out is what Anthropic calls context compaction. It can automatically summarize older parts of the conversation to keep working well beyond the window limit. It also supports up to 128k output tokens, meaning it can generate massive responses in a single pass. And finally, Claude Sonnet 4.6. This is Anthropic's more accessible model, and it's punching well above its weight class. It also features a 200k standard window with 1 million tokens in beta. In early testing, users actually preferred Sonnet 4.6 over the previous OPUS 4.5 model 59% of the time. So let me give you the quick rundown. Gemini 3 Pro, 1 million tokens standard, up to 2 million Enterprise. GPT 5.2 thinking, 196k tokens for paid users. Claude Opus 4.6, 200k standard, 1 million in beta. Claude Sonnet 4.6, 200k standard, 1 million in beta. Google leads on raw context size. OpenAI tiers access by subscription plan. And Anthropic gives you the same generous window on both their flagship and mid-tier model. Here's my advice. Match the model to the task. For quick Q ⁇ A or drafting an email, context size barely matters. But if you're feeding in a 40-page curriculum guide or an entire policy manual and asking the AI to analyze it, you need a large window model, or you need to chunk your input strategically. And remember, advertised context windows and effective context windows aren't the same thing. A bigger window doesn't automatically mean better answers. It just means the model can hold more information. The quality of your prompt still drives the quality of the output. That's the 2026 breakdown. Understanding context windows will make you a more effective AI user. Whether you're a teacher building lesson plans, an administrator drafting strategy, or a student doing research. Stay curious, and I'll see you in the next one.
