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LLM context window limits: maximum tokens for every major model in 2026

Context window size is a spec, not a guarantee of quality. Llama 4 Scout's official 10M window is five times a 2M window, but that doesn't mean every deployment accepts the full amount or uses it equally well. For production applications, verify the endpoint limit and benchmark your specific task at multiple context lengths.

A model context limit is a token budget, but providers may publish separate input and output limits. Meta lists Llama 4 Scout with a 10 million token context window, far above the 128K limit of Llama 3.1. Check the exact model version and endpoint because hosted services can expose a smaller limit. Long context can cost more and increase latency, and a larger supported window does not guarantee better retrieval or reasoning.

By TechCompare · Updated

Knowledge area
Model Comparison
Tokenization across model families
Topic focus
Token limits by model
token-limits
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How this is calculated

Use the maintained model entries in the pricing calculator and each provider specification when checking limits. Llama 4 cannot be collapsed to the 128K figure associated with Llama 3: Meta's official material lists Scout at 10M tokens. Model generations and variants differ substantially, including within Gemini and Llama families. Do not assume that a family name establishes one context limit, or that an input-token limit is interchangeable with the maximum combined request budget.

Verdict

Budget the actual input, chat formatting, tool definitions, and requested output against the selected endpoint limits. Test retrieval and answer quality at representative lengths. A smaller, relevant prompt can outperform a larger context filled with unrelated material.

More Tokens scenarios

What are tokens
A token is the atomic unit of text that a language model processes.
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OpenAI vs Llama tokenizer
A sentence that costs 50 tokens on GPT-5 might cost 55 tokens on Llama 4 or 48 on Claude.
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Prompt optimization
Prompt optimization aims to get the same or better results with fewer input tokens.
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Related guides

Context window explained
How the model's context window is the ceiling these token limits sit under.
View guide ➜
Llama 4 Scout at 10M context
The memory cost of using Scout's official 10M token window.
View guide ➜
Llama 3.1 8B at 128K context
VRAM required for a 128K context window at the model's token limit.
View guide ➜

Frequently asked questions

Which LLM has the largest context window in 2026?
Among the models covered here, Meta officially lists Llama 4 Scout with a 10 million token context window. That's five times a 2M window and about 78 times Llama 3.1's 128K limit. Hosted endpoints may cap Scout below its model specification, so verify the exact service and reserve space for output, tools, and chat formatting.
Does a bigger context window mean better answers?
Not automatically. The 'lost in the middle' effect means models often under-attend to content buried halfway through a very long input, and cost plus latency scale with every token you stuff in. For many tasks, a well-chosen 64K window beats a lazy 256K dump, both in quality and in price.
How much text fits in a 128K token window?
Roughly 96,000 English words, call it a 300-page novel, about 200 pages of text, or a full mid-size code repository with the tests. Budget headroom for the output too: a 100K-token document leaves only 28K for the model's response and follow-up messages inside a 128K window.

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