Context Window Token Limits for Every Major LLM in 2026

Context window size has become a major LLM differentiator in 2026. Gemini 2.5 Pro can hold 2 million tokens. GPT-4o tops out at 128K. The right pick depends on what you actually send. Here is the full table for every major model.

Full Context Window Comparison — April 2026

Model	Provider	Context window	Max output	Equivalent in words
GPT-4o	OpenAI	128K	16K	~96,000 words
GPT-4.1	OpenAI	128K	32K	~96,000 words
GPT-4o mini	OpenAI	128K	16K	~96,000 words
GPT-4.1 mini	OpenAI	128K	32K	~96,000 words
GPT-4.1 nano	OpenAI	128K	32K	~96,000 words
o3	OpenAI	128K	32K	~96,000 words
o3 mini	OpenAI	128K	32K	~96,000 words
o4 mini	OpenAI	128K	32K	~96,000 words
Claude Haiku 3.5	Anthropic	200K	8K	~150,000 words
Claude Sonnet 4	Anthropic	200K	64K	~150,000 words
Claude Opus 4	Anthropic	200K	32K	~150,000 words
Gemini 2.0 Flash	Google	1M	8K	~750,000 words
Gemini 2.5 Flash	Google	1M	8K	~750,000 words
Gemini 2.5 Pro	Google	2M	8K	~1,500,000 words
DeepSeek V3	DeepSeek	128K	8K	~96,000 words
DeepSeek R1	DeepSeek	128K	8K	~96,000 words
Llama 4 Scout	Meta	10M (theory) / 1M (practical)	8K	~750,000 words
Llama 4 Maverick	Meta	1M	8K	~750,000 words
Mistral Large	Mistral	128K	8K	~96,000 words
Grok 3	xAI	1M	8K	~750,000 words

What "Context Window" Actually Means

The context window is the total token budget for one request. It includes:

• System prompt — your model instructions
• Conversation history — past messages in a chat
• Retrieved context — chunks from a vector database (RAG)
• User message — the current question
• Output — what the model generates in response

All five share the same window. Send a 100K token document with a 50K token chat history and there's only 50K left for output and other content (assuming a 200K window). Plan accordingly.

Real-World Equivalents for Each Window Size

Window	Pages of text	Real example
8K	15-20 pages	One short essay or document
32K	60-80 pages	One short report
128K	240-320 pages	One short novel
200K	375-500 pages	One full novel
1M	1,800-2,500 pages	5-7 novels or one long textbook
2M	3,600-5,000 pages	15-20 novels or one encyclopedia volume
10M	18,000-25,000 pages	100+ novels or a large code repository

When Each Window Size Makes Sense

8K-32K (older or specialized models): Fine for single-question Q&A, simple chat, code completion. Most use cases don't need more.

128K (GPT-4o, GPT-4.1, DeepSeek, Mistral Large): The new default. Handles long chat histories, multi-turn conversations, document analysis up to ~80K words. 95% of production workloads fit here.

200K (Claude Sonnet 4, Opus 4): Adds headroom for long documents (50-page reports), complex multi-turn agent workflows, and large code reviews. Worth using when you need 100K+ tokens of input.

1M (Gemini Flash, Llama 4 Maverick, Grok 3): Specialized for long-document workloads — full books, large codebases, extensive document collections. The price-per-token at this scale matters more than the window itself.

2M (Gemini 2.5 Pro): Specialty use cases. Multi-document research, very long-form content, comprehensive code analysis. Most teams will never need this.

The "Effective" Context Window

Just because a model accepts 200K tokens doesn't mean it uses them well. Long-context performance varies. Some models suffer from "lost in the middle" — the model attends to the start and end of long inputs but glosses over the middle.

For long-context use:

• Claude tends to use long context the most reliably
• Gemini handles very long context but quality varies by task
• GPT-4o at 128K performs well but degrades past 100K input
• Llama and DeepSeek at 128K are good for typical loads, weaker on very long context

If you depend on the model finding details in the middle of a long input, test with realistic prompts before committing.

How to Use the Whole Window Without Wasting It

Five practices:

1. Put critical info at the start AND end. Models attend to both more reliably than the middle.
2. Use clear structure. Headers, sections, numbered lists help the model navigate long input.
3. Reference specific sections in your question. "Based on Section 3 of the document..." performs better than open-ended questions.
4. Cache static prefixes. If the same long context appears across many queries, use prompt caching for big discounts.
5. Verify your input fits. Use the Token Counter before sending to avoid window-exceeded errors.