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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. A 2M token window lets you submit 2M tokens. Whether the model pays attention to all of them is a different question. For production applications, benchmark your specific task at multiple context lengths. You may find that 64K tokens performs better than 256K.

Context window size is the maximum number of tokens a model can process in a single request, including both input and output. In 2026, leading models range from 128K tokens (GPT-5.4 Mini) to 2 million tokens (Gemini 3.1 Pro and 3.5 Flash). Bigger isn't always better: long context windows cost more, run slower, and models can lose attention to details in the middle of very long inputs.

By TechCompare · Updated

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

GPT-5.4 (OpenAI): 256K tokens. GPT-5.4 Mini: 128K. GPT-5.5: 256K. Claude Opus 4.8 (Anthropic): 500K. Claude Sonnet 4.6: 200K. Gemini 3.1 Pro (Google): 2M. Gemini 3.5 Flash: 2M. Llama 4 (Meta): 128K (open-weight). DeepSeek V4 Pro: 1M. The trend is upward: a 2M token context window can fit roughly 1.5 million words, about three times the entire Lord of the Rings trilogy in a single prompt. But effective context utilization drops as context length increases. The 'lost in the middle' problem means models often ignore content in the middle of long contexts. For most use cases, 128K tokens is more than enough.

Verdict

The range in 2026 spans an order of magnitude. GPT-5.4 Mini tops out at 128K, Claude Opus 4.8 reaches 500K, and Gemini 3.1 Pro and 3.5 Flash both hit 2M, which is enough for a 1.5-million-word novel in one call. The 'lost in the middle' effect means content in the upper half of long contexts often gets ignored, so 64K can beat 256K on attention-critical tasks. Cost and latency scale with length, not just quality.

More Tokens scenarios

What are tokens
A token is the atomic unit of text that a language model processes.
View details ➜
OpenAI vs Llama tokenizer
A sentence that costs 50 tokens on GPT-5 might cost 55 tokens on Llama 4 or 48 on Claude.
View details ➜
Prompt optimization
Prompt optimization is the practice of getting the same or better results from an LLM with fewer input tokens.
View details ➜

Related guides

Context window explained
How the model's context window is the ceiling these token limits sit under.
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?
Gemini 3.1 Pro and 3.5 Flash at 2 million tokens, enough for roughly 1.5 million words in one prompt. DeepSeek V4 Pro sits at 1M. Claude Opus 4.8 offers 500K, GPT-5.4 and GPT-5.5 offer 256K, and Llama 4 open weights run 128K. The gap between the top and bottom of that list is 15x.
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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