AI Context Window Size Comparison: Choose for Your Task

AI context windows range from a few thousand tokens on lightweight models to over a million on the largest. The number matters, but it does not tell the whole story. A model with a 128,000-token window and strong recall across all of it is more useful for long-document work than one with a million-token window that loses precision in the middle. For work that spans sessions, models or people, fenbs keeps the important context — decisions, tasks, rules and current status — outside any single window, so it is always available regardless of which model you use.

This comparison focuses on the questions that matter for real tasks: how much can you put in, how reliably does the model use it all, and what does effective capacity look like for writing, coding and research work?

What a context window actually is

A context window is the total amount of text a model can read and reason about in a single request. Everything you send — your message, any system instructions, uploaded files, and the conversation history — must fit inside it. Tokens are roughly pieces of words: 1,000 tokens is approximately 750 English words, though the conversion varies by language and content type.

The window has two parts: input (what you send) and output (what the model generates). A model described as having a 128,000-token context might allow 128,000 tokens total, or 127,000 in and 4,000 out, or some other split. Check the model documentation for the input and output limits separately, since the output ceiling can be a binding constraint for tasks that need long responses. For a deeper explanation of context windows, see the AI context window guide.

How advertised size differs from effective capacity

Advertised context window size is the maximum, not a guarantee of uniform quality across all positions. Research into how large-language models use long contexts shows that models tend to pay close attention to text near the start and end of the context, while information in the middle of very long inputs can receive less attention. This is sometimes called the "lost-in-the-middle" effect.

What this means in practice: if you have a 200-page document and the answers you need are buried on page 100, a model with a nominally large window may still miss them. Structuring inputs so the critical information is near the beginning or end — rather than hoping the model finds it anywhere in 100,000 tokens — usually produces better results. For more on how context quality degrades, see AI context rot.

Context window sizes for major models

Model context sizes change with each release. The figures below reflect published sizes at the time of writing; check each provider's documentation for current numbers, since these are updated regularly.

Choosing by task type

For coding, the relevant question is whether the model can see your full codebase at once, or whether you need to pass in specific files. Most repositories require selective context rather than a full dump. A 128K-token window covers a large amount of code, but structured context management — sending only the relevant files and functions — usually outperforms maximizing the raw input size.

For document analysis, the critical factor is whether the model can reliably retrieve specific facts from long documents. A practical test: send the document, then ask three specific questions whose answers appear in different parts. Compare the accuracy before deciding the window size is sufficient for your use case. The AI context files compared guide covers how different content types perform in context.

For research and writing, context windows matter less than for long-document analysis. Most writing sessions are well within any modern model's window. The bigger constraint is often the output limit: how long a well-structured document the model can generate in one response. If output limits are a concern, look for the model's maximum output tokens alongside its input window size.

What to put in the window versus what to keep outside it

The most effective use of a context window sends specific, relevant material rather than everything available. A session working on a feature design does not need the full product specification; it needs the section that describes this feature, the approved decisions that constrain the design and the current task with its open questions.

What does not belong in the window for every session: background documentation that rarely changes, decisions from previous projects, full chat histories and large reference documents. Those slow down the request and can displace the content the model actually needs. Keep them accessible but load them selectively. The context engineering for AI agents guide explains the approach in more detail.

Decisions, rules and project progress belong outside the window entirely — in a record that persists between sessions regardless of which model you use. That way you never need to rechoose what to load; the structured context is always current and always in the right shape for the next assistant to read.

How to do this with fenbs

1. Create a free account and add a project with its goal, current tasks and any standing decisions or rules.
2. Connect your AI app from Integrations. Claude, ChatGPT, Cursor and others connect over MCP and read the fenbs project context at the start of a session.
3. Ask the assistant to load the project context before beginning any work: Rules first, then Where we left off, current tasks and any relevant context notes.
4. Keep large reference documents where they live (a document tool, a repository) and link to them from the project. Load specific sections into the context window when needed, not the whole document every session.
5. At the end of the session, ask the assistant to save progress in the project so the next session — with any connected model — starts from the current state.