Task-based recommendation
Best LLM for RAG & Document Analysis
Compare AI models for retrieval-augmented generation, document QA, and long-document analysis. We evaluate context utilization, citation accuracy, and reasoning quality.
Last updated: May 2025 · Methodology
All benchmark scores, pricing data, and rankings on this page are mock placeholders for development and preview purposes. They do not reflect real-world model performance. Real data sources will be connected as the product matures.
Our Pick
Gemini 2.5 Pro — Best for RAG
With a 1M token context window and strong reasoning, Gemini 2.5 Pro excels at processing massive document sets. Claude Sonnet 4.6 is the better choice when citation accuracy and nuanced understanding matter most.
Compare RAG models →Top RAG Models
| Rank | Model | Provider | Notes |
|---|---|---|---|
| 1 | Claude Sonnet 4.6 | Anthropic | Excellent document understanding with 200K context. |
| 2 | Gemini 2.5 Pro | 1M context window. Best for massive document analysis. | |
| 3 | Command R+ | Cohere | Purpose-built for RAG. Strong retrieval and citation. |
| 4 | GPT-4.1 | OpenAI | Strong document QA with reliable structured output for citations. |
| 5 | DeepSeek V3 | DeepSeek | Good RAG at very low cost. 128K context. |
| 6 | Mistral Large 3 | Mistral | 262K context window. Strong multilingual RAG. |
MVP placeholder. Full RAG benchmark data coming soon. See full leaderboard.