Best For
Best LLM for Long Documents in 2026
Context window size is the deciding factor when your document is longer than a model can hold in memory. Below that limit, quality and coherence matter more. Here is how the major models stack up on both dimensions.
Updated February 2026
What actually matters for long documents
Before we get to the pick — the criteria that separate good from bad here:
Context window size — The document has to fit. A 100-page PDF is roughly 75,000 tokens. Models with 128K windows handle it; models with 32K don't. This is the first filter — anything else is irrelevant if the doc won't load.
Mid-context retrieval — Most models are good at the beginning and end of their context window — and unreliable in the middle. This is called the 'lost in the middle' problem. It matters a lot when key facts are buried halfway through a document.
Summarization faithfulness — Does the summary accurately represent what's in the document, or does it hallucinate details and smooth over contradictions? For legal, financial, or medical documents, accuracy beats fluency.
Cost at scale — Long documents mean lots of input tokens. At $15/1M input tokens, processing a 100K token document costs $1.50. If you're processing hundreds of documents, those costs compound fast.
Our pick
Llama 4 Scout's 10 million token context window is in a category of its own — roughly 7.5 million words. Entire book series, massive codebases, or years of email history fit in a single prompt. Open weights mean you can self-host for maximum privacy. The intelligence gap vs frontier models is real but the context advantage is dramatic.
Pricing: API via Groq: $0.11/$0.11 per 1M tokens. Free tier available on Groq. Self-host with GPU hardware.
Also consider
Gemini 3.1 Pro matches Gemini 3 Pro's 1M token context window but pairs it with the highest intelligence score of any model (AA Index 57 vs 48.44 for Gemini 3 Pro). For long-document tasks where you need not just to load a large document but to reason accurately about it — synthesizing findings, identifying contradictions, answering complex questions across a massive corpus — 3.1 Pro delivers substantially better analysis at the same price.
API at $2/$12 per 1M tokens (≤200K context). Over 200K tokens: $4/$18 per 1M. Google AI Studio developer free tier (rate-limited).
Full review →Gemini 3 Pro's 1 million token context window handles full books, large codebases, and multi-document research sets. Lower latency than Gemini 3.1 Pro for interactive use. Note: Gemini 3 Pro is deprecated March 9, 2026 — for new long-context projects, Gemini 3.1 Pro offers the same 1M window with significantly better reasoning at the same price. The free consumer product at gemini.google.com runs Flash, not Pro.
Free via gemini.google.com. API at $2/$12 per 1M tokens (2× rate for prompts over 200K tokens).
Full review →Grok 4.1's 2 million token context window is the second-largest among commercial models and covers most enterprise long-document use cases. Strong on reasoning across long contexts. The real-time X data access adds unique capability for research tasks that need current information alongside large documents.
Via X Premium ($8/month) or SuperGrok ($30/month). API: Grok 4.1 Fast at $0.20/$0.50 per 1M tokens; Grok 4 reasoning at $3/$15 per 1M tokens.
Full review →Bottom line
For documents over 400K tokens: Gemini 3.1 Pro (most capable, same 1M context as 3 Pro) or Gemini 3 Pro (lower latency, but deprecated March 9, 2026 — migrate to 3.1 Pro) or Llama 4 Scout (open-source, privacy-first, 10M context). For documents between 128K and 400K: GPT-5.2 or Claude Sonnet depending on whether you prioritize raw capability or coherence. For anything under 128K, context window is not the deciding factor — pick based on task type.
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