The top AI models for Retrieval-Augmented Generation, ranked by a RAG-weighted composite score. Models are scored with bonuses for large context windows (fitting more retrieved chunks), structured JSON output (parsing extracted data), function calling (tool-based retrieval), and streaming (real-time answers). Updated hourly from 366+ models.
340
Total Models
278
128K+ Context
255
With JSON Mode
270
Function Calling
34
Free Models
| # | Model | Score |
|---|---|---|
| 1 | Claude Opus 4.7Anthropic | 118 |
| 2 | GPT-5.5OpenAI | 116 |
| 3 | Gemini 3.1 Pro Preview Custom ToolsGoogle | 115 |
| 4 | Gemini 3.1 Pro PreviewGoogle | 115 |
| 5 | GPT-5.4 ProOpenAI | 115 |
| 6 | GPT-5.4OpenAI | 115 |
| 7 | GPT-5.5 ProOpenAI | 114 |
| 8 | GPT-5.2 ProOpenAI | 114 |
| 9 | Claude Opus 4.6 (Fast)Anthropic | 113 |
| 10 | Claude Opus 4.6Anthropic | 113 |
| 11 | GPT-5.2-CodexOpenAI | 113 |
| 12 | GPT-5.2OpenAI | 113 |
| 13 | Grok 4.20xAI | 112 |
| 14 | GPT-5.3-CodexOpenAI | 112 |
| 15 | GPT-5 ProOpenAI | 112 |
| 16 | Gemini 3 Flash PreviewGoogle | 111 |
| 17 | Grok 4xAI | 111 |
| 18 | GPT-5.1-Codex-MaxOpenAI | 111 |
| 19 | GPT-5 CodexOpenAI | 111 |
| 20 | GPT-5OpenAI | 111 |
| 21 | GPT-5.3 ChatOpenAI | 110 |
| 22 | GPT-5.1OpenAI | 110 |
| 23 | GPT-5.1-CodexOpenAI | 110 |
| 24 | GPT-5.1-Codex-MiniOpenAI | 110 |
| 25 | DeepSeek V4 ProDeepSeek | 110 |
| 26 | o3 Deep ResearchOpenAI | 110 |
| 27 | o3 ProOpenAI | 110 |
| 28 | o3OpenAI | 110 |
| 29 | GPT-5.1 ChatOpenAI | 110 |
| 30 | Claude Sonnet 4.6Anthropic | 108 |
RAG pipelines retrieve relevant chunks from a knowledge base and inject them into the prompt. Models with 128K+ token context windows can fit more retrieved passages alongside the user query, reducing information loss and improving answer quality. Larger context also enables multi-document synthesis across dozens of retrieved chunks simultaneously.
JSON mode ensures the model returns well-formed structured data instead of free-text prose. For RAG applications, this is critical when extracting entities, citations, or metadata from retrieved documents. Structured output makes it easy to parse responses, populate UIs, and feed results into downstream systems reliably.
Function calling lets the model invoke retrieval tools dynamically - querying vector databases, searching knowledge bases, or fetching documents mid-conversation. This enables agentic RAG architectures where the model decides what to retrieve, how many chunks to pull, and when to do follow-up searches for better answers.
RAG applications process large volumes of tokens per query - retrieved chunks plus the question plus the generated answer. At scale, input and output token costs add up fast. Models with competitive per-million-token pricing let you run RAG pipelines in production without excessive API bills, especially for high-traffic document Q&A systems.
Discover models by specific RAG capabilities, or compare top models head-to-head on the full leaderboard.
Match your embedding model to your retrieval needs. OpenAI text-embedding-3-large and Cohere embed-v3 lead for English. For multilingual RAG, use models with cross-lingual embeddings. The generation model matters less than retrieval quality - even smaller models produce great answers from well-retrieved context.
16K-32K tokens handles most RAG use cases (5-10 retrieved chunks plus query and instructions). For complex multi-document synthesis, 128K+ helps. Gemini's 1M context enables whole-document-collection RAG but increases cost. Optimize chunk size and retrieval quality before scaling context.
Mid-tier models (GPT-4o Mini, Claude Haiku, Gemini Flash) offer the best cost-performance for RAG since retrieved context does the heavy lifting. Reserve expensive models for synthesis-heavy queries. Most production RAG systems spend 80% of budget on retrieval infrastructure, not generation.
Use models with high factual grounding scores and citation capabilities. Instruct the model to only answer from provided context and say 'I don't know' otherwise. Implement answer verification by checking claims against source chunks. Models with JSON output help structure citations for verification.