Quick takeaways
- Gemini 3.1 Pro and Claude Opus lead for very long documents and codebase-wide analysis.
- GPT-4o is a strong daily option with reliable recall up to its context limit.
- Llama 4 and Mistral Large offer competitive open-weight options for self-hosted teams.
- For massive corpora, pair a large context model with retrieval to control cost and accuracy.
How to think about AI model choice
Andrej Karpathy explains what large language models are good at and how to pick the right model for a task.
Top picks
Gemini 3.1 Pro
Largest context window and strong recall for books, legal bundles, and multi-document review.
Claude Opus 4.8
Careful reasoning across long texts; great for contracts, research, and detailed summaries.
GPT-4o
Fast, reliable, and well-integrated for documents within its context limit.
Llama 4
Self-hostable with a large context option and no per-token API costs.
Model comparison
| Model | Best for | Context strength | Considerations |
|---|---|---|---|
| Gemini 3.1 Pro | Books, legal bundles, multi-doc review | Very large context, strong needle-in-haystack recall | Pricing tiered by model size |
| Claude Opus 4.8 | Contracts, research synthesis | Large context, high-quality reasoning | Slower and more expensive |
| GPT-4o | Daily long-document tasks | Reliable recall, fast, broad tool support | Smaller context than Gemini or Claude |
| Llama 4 | Self-hosted long-context work | Open weights, customizable context | Needs GPU infrastructure and tuning |
| Mistral Large 3 | European-hosted deployments | Strong retrieval and summarization | Smaller ecosystem than OpenAI |
How to pick
Without AI vs. with AI
| Task | Without AI | With AI |
|---|---|---|
| Reading long PDFs | Analysts scroll, highlight, and take notes across hundreds of pages. | Long-context models ingest the full PDF and answer specific questions. |
| Contract review | Lawyers compare clauses across multiple documents manually. | Models compare documents in one prompt and flag inconsistencies. |
| Book summaries | Readers extract themes chapter by chapter. | Models summarize entire books and map themes across the text. |
| Codebase analysis | Developers open files one by one to trace logic. | Long-context models read many files and explain cross-file relationships. |
| Research synthesis | Researchers maintain spreadsheets of findings from papers. | Models synthesize findings across many papers in one conversation. |
FAQ
What is the best AI for reading long PDFs?
Claude Opus and Gemini 3.1 Pro handle long PDFs well. GPT-4o works for shorter documents and is faster for quick questions.
Does a larger context window always mean better results?
Not always. Needle-in-haystack recall, reasoning quality, and cost also matter. Test your actual documents rather than trusting the advertised token count.
Is RAG better than a long context window?
For very large corpora, RAG is usually cheaper and more accurate. For deep analysis of a single long document, a large context model is simpler.
Which model is best for legal contracts?
Claude Opus and Gemini 3.1 Pro are popular for contract review because they handle long, dense text and nuanced language.
How do I reduce long-context API costs?
Summarize first, use retrieval to limit input, batch queries, and cache repeated context where the provider supports it.
What counts as a long context?
Generally 100K+ tokens, enough for books, codebases, or many long documents.
Which model has the largest context window?
Gemini 3.1 Pro is known for very large context windows; verify current specs on Google's site.
Do long-context models forget information?
They can struggle with needle-in-haystack recall; test on your actual documents.
Is RAG cheaper than long context?
For very large corpora, RAG is usually cheaper. For deep analysis of one document, long context is simpler.
Can I trust long-context summaries?
Use them as starting points. Verify key claims against the original document.