Which AI Models Have the Largest Context Windows in 2026?
See which models have the largest context windows in 2026 and how much that actually changes what they can do.
Context windows have become one of the easiest AI specifications to compare and yet one of the easiest to misunderstand.
A larger context window lets a model work with more information during a single request or conversation. That can include previous messages, documents, code, webpages, system instructions, and other material supplied to the model.
In 2026, one million token context windows have become common among frontier models. A few models go considerably further. Meta's Llama 4 Scout supports a published context window of 10 million tokens, while current models from OpenAI, Anthropic, Google, and xAI sit around the one million token range.
The biggest number does not automatically make a model the best choice. Long context becomes useful when the model can keep track of the information that matters, retrieve details accurately, and work with the context without unnecessary cost or latency.
Largest AI Context Windows Compared
Sigma Browser appears first because it is our preferred way to apply these capabilities during real web work. Sigma Browser is not itself an AI model, however it supports cloud and local AI workflows. Its built-in tools include AI Chat, Chat with Page, Deep Research, Agent, and local AI options.
Which AI Has the Largest Context Window in 2026?
Among the widely available models with official specifications we reviewed, Llama 4 Scout has the largest published context window at 10 million tokens.
Meta describes Llama 4 Scout as a model designed for unusually large inputs, including multi-document summarization and reasoning over extensive codebases. Meta also says Scout was pretrained and post-trained at 256K context, with length generalization extending its supported context to 10 million tokens.
Most other major frontier models currently cluster around one million tokens.
GPT-5.6 Sol supports a 1.05 million token context window. Claude Opus 5 and Claude Sonnet 5 support one million tokens. Gemini 3.5 Flash supports 1,048,576 input tokens, while Grok 4.3 supports one million tokens.
Our Favorite Way to Use Long-Context AI on the Web
A large context window is most useful when getting information into the model does not become a separate job.
That is why Sigma Browser is our favorite option for long-context work that starts on the web.
Chat with Page lets you work directly with the webpage you already have open. Deep Research can work across multiple sources, while AI Chat provides a general AI workspace inside the browser. Sigma also supports local AI for supported workflows that need to run on the device.
This matters for long-context tasks because much of the information people want an AI model to understand already lives in the browser. Research papers, documentation, reports, product pages, articles, and other sources can all become part of the workflow.
What Is a Context Window in AI?
A context window is the amount of information an AI model can keep available while processing a request.
The window is measured in tokens rather than words. Tokens can represent whole words, parts of words, punctuation, numbers, code, and other input.
A conversation can consume context through several sources. Previous messages take up space. So do uploaded documents, instructions, tool results, webpage content, and the model's generated output.
Once enough material accumulates, the context limit becomes relevant.
A model with a 128K window can work with considerably more information at once than a model with a 32K window. Moving from 128K to one million tokens changes the kinds of tasks that can fit inside a single context. Ten million tokens pushes that much further.
How Big Is a One Million Token Context Window?
The exact number of words varies because tokenization depends on language, formatting, code, numbers, and the tokenizer used by the model. In English prose, one million tokens can represent several hundred thousand words.
That is enough space for large document collections, substantial codebases, long research sessions, or many individual webpages.
It also changes how AI applications can be built. Developers can sometimes provide much larger portions of the original material directly instead of splitting every source into small fragments first.
Large windows still benefit from retrieval systems, summaries, and careful context management. Processing unnecessary context can increase both cost and latency.
What Can You Do With a 10 Million Token Context Window?
A window this large opens the door to unusually large workloads.
A developer could work with a substantial repository without aggressively cutting down the source material. A research system could provide large collections of reports or documents in the same context. Long-running agents could also retain more history before older information needs to be summarized or removed.
Whether using the full 10 million tokens is practical depends on infrastructure, cost, latency, and the task itself.
Does a Bigger Context Window Make an AI Model Better?
No.
Context size tells you how much information the model can accept. It does not tell you how intelligent the model is, how accurately it uses every detail, or how well it performs on a particular task.
A huge input can contain hundreds of thousands of irrelevant tokens surrounding the few pieces of information that actually matter.
Cost is another consideration. Sending hundreds of thousands of tokens repeatedly can become expensive, especially in long-running applications.
A smaller and carefully selected context can therefore be more efficient than filling the entire available window.
What Happens When the Context Window Is Full?
An AI model cannot keep adding information to a fixed context forever.
What happens next depends on the application and model interface.
An API request that exceeds the supported window may fail. Mistral's documentation explicitly states that requests exceeding the model's maximum context return an error.
Chat products and agent systems can handle long sessions differently. They may summarize older conversation history, remove older material, retrieve relevant information when it becomes necessary, or begin a fresh context while saving state elsewhere.
This explains why an AI assistant may appear to lose an earlier detail during a very long conversation. The complete original history may no longer be present in the active model context.
Is a 200K Context Window Still Enough?
For many tasks, yes.
A 200K or 256K context window is already large enough for substantial documents and conversations.
The million-token category becomes more useful when working across very large repositories, many documents, extensive tool output, or long-running agent sessions.
Ten million tokens makes even larger workloads possible, but very few normal chat conversations require anything close to that amount.
The useful context size depends on the work being done.
If you need to summarize one article, one million tokens provides little advantage over a smaller window. If you need to compare a large collection of documents without constantly discarding context, the difference can become significant.
Which Model Should You Choose for Long Context?
If maximum published context size is the priority, Llama 4 Scout is the standout choice in this comparison with 10 million tokens.
GPT-5.6, Claude Opus 5, Claude Sonnet 5, Gemini 3.5 Flash, and Grok 4.3 all sit around the one million token level and offer different strengths beyond context capacity.
For browser-based work, our favorite is Sigma Browser. The browser keeps AI tools close to the pages, files, and research material being used, while supporting both cloud and local AI workflows.
The final choice should come from the task itself. Context size matters most when your workload is large enough to use it.
Final Takeaway
Context windows have grown dramatically.
Llama 4 Scout pushes the published maximum to 10 million tokens. OpenAI, Anthropic, Google, and xAI offer major models around the one million token level, while 256K remains common across other capable model families.
These larger windows make it possible to work with more documents, code, conversation history, and web content at the same time.
The context number should still be treated as one specification among many. Reasoning quality, retrieval accuracy, cost, latency, privacy, and the surrounding workflow all influence how useful that context becomes.
For web-heavy tasks, Sigma Browser is our preferred environment. Chat with Page, Deep Research, AI Agent, AI Chat, and local AI keep the AI workflow close to the information being used.
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