Best AI Models for Research & Analysis
Compare AI models for research tasks — analyzing documents, synthesizing information, answering expert questions. Find the best AI for academic and professional research.
Our Top Picks
A 1M token context window combined with built-in thinking mode makes it the top pick for research. Upload entire papers, legal filings, or datasets and interrogate the full corpus in one prompt.
Excellent reasoning, GPQA score of 65.0 (graduate-level questions), and a 200K context for long documents.
1M context window at $0.30/1M tokens. Ideal for processing large document collections where quality-per-dollar matters.
What We Looked At
- GPQA benchmark
- Context window
- Reasoning depth
- Citation accuracy
- Multimodal (PDF, images)
GPQA: graduate-level research questions
GPQA (Graduate-Level Google-Proof Q&A) tests questions hard enough that PhD-level domain experts answer them correctly only about 65% of the time. Claude Sonnet hits 65.0% — operating at roughly human-expert level on domain-specific science. GPT-4o scores around 53%, Gemini 2.5 Pro around 65%. If you're doing expert-level domain research — medicine, law, advanced engineering — both Claude and Gemini 2.5 Pro are meaningfully ahead of the field.
Processing long documents
Gemini 2.5 Pro's 1M token context is the practical answer to 'I have too much to read.' A 400-page legal filing, a corpus of 50 research papers, an entire GitHub repository — all in one prompt. Its built-in thinking mode also means it reasons across the content rather than just retrieving it. For documents that fit in Claude's 200K window, Claude gives sharper nuanced extraction. Above 200K, Gemini 2.5 Pro is the only mainstream option.
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