LLM Leaderboard for RAG
Performance comparison of the top LLMs for Retrieval-Augmented Generation (RAG), tested on diverse datasets.
Last updated: December 11, 2025
| Compare | ||||||
|---|---|---|---|---|---|---|
1711 | 16191 | $1.250 | Proprietary | 400K | ||
1657 | 5851 | $0.200 | Proprietary | 2000K | ||
1619 | 8252 | $5.000 | Proprietary | 200K | ||
1588 | 5380 | $1.750 | Proprietary | 400K | ||
1522 | 17903 | $2.000 | Proprietary | 1049K | ||
1489 | 33116 | $0.400 | MIT | 203K | ||
1429 | 15199 | $1.250 | Proprietary | 1049K | ||
1338 | 18271 | $0.300 | MIT | 164K | ||
1331 | 12312 | $0.051 | Apache 2.0 | 33K | ||
1316 | 11199 | $0.039 | Apache 2.0 | 131K |
Overview
Our Recommendation
We recommend GPT-5.1 as the best overall LLM for RAG applications.
Highest Win Rate
Outperforms other models in head-to-head comparisons across diverse RAG benchmarks with a 65.6% win rate.
Excellent Grounding
Achieves 4.97 average grounding and faithfulness scores, ensuring responses stay true to retrieved documents.
400K Context Window
Large context window handles substantial retrieved document sets with adaptive reasoning for complex queries.
Understanding LLMs
What are LLMs in RAG?
LLMs in RAG Systems
Large Language Models (LLMs) are the reasoning engine in Retrieval-Augmented Generation systems. After your retrieval pipeline fetches relevant documents, the LLM synthesizes that context into coherent, accurate responses. The quality of your RAG output depends heavily on choosing an LLM that excels at grounding responses in retrieved information while maintaining reasoning capabilities.
Key Metrics for RAG Performance
When evaluating LLMs for RAG, we focus on five critical dimensions: correctness (factual accuracy), faithfulness (staying true to source material), grounding (citing retrieved context), relevance (addressing the query), and completeness (covering all aspects). Models that score high across these metrics deliver better RAG experiences with fewer hallucinations.
Context Window Considerations
Context window size determines how much retrieved content your LLM can process. While larger windows (1M+ tokens) handle more documents, models with 128K-400K windows often provide better cost-performance for most RAG applications. The key is matching window size to your typical retrieval volume and query complexity.
Selection Guide
Choosing the right LLM
For Maximum Accuracy
Best for:
- • Customer-facing chatbots
- • High-stakes decision support
- • Complex knowledge base queries
For Self-Hosting
Best for:
- • Data privacy requirements
- • Cost-sensitive applications
- • Custom fine-tuning needs
For Low Latency
Best for:
- • Real-time chat applications
- • Mobile applications
- • High-concurrency scenarios
For Large Context
Best for:
- • Long document analysis
- • Multi-document synthesis
- • Comprehensive knowledge retrieval
For Multilingual Support
Best for:
- • Global applications
- • Cross-lingual knowledge bases
- • Multilingual customer support
Methodology
How We Evaluate LLMs for RAG
The LLM Leaderboard tests models on three diverse RAG datasets — MSMARCO (web search), PG (long-form content), and SciFact (scientific claims) — to evaluate how well they synthesize retrieved information into accurate responses.
Testing Process
Each LLM receives the same set of queries with retrieved documents from FAISS-based retrieval. We measure response quality across five dimensions (correctness, faithfulness, grounding, relevance, completeness) and track latency to capture real-world RAG performance.
ELO Score
For each query, GPT-5 evaluates two model responses and selects the better answer based on RAG quality criteria. Wins and losses feed into an ELO rating — higher scores indicate more consistent high-quality RAG outputs.
Evaluation Metrics
We assess correctness (factual accuracy), faithfulness (adherence to sources), grounding (citing context), relevance (query alignment), and completeness (thorough coverage). These metrics together measure how well an LLM performs the core RAG task of generating accurate, well-grounded responses.
Common questions
LLM for RAG FAQ
- What makes a good LLM for RAG?
- The best LLMs for RAG excel at grounding responses in retrieved documents while maintaining factual accuracy. They should have sufficient context window for your retrieval volume, strong reasoning capabilities, and consistent performance across different content types.
- How much does context window size matter?
- Context window size determines how many retrieved documents you can include. While larger windows (1M+ tokens) offer flexibility, most RAG applications work well with 128K-400K windows. The key is matching window size to your typical retrieval volume and using good reranking to select the most relevant documents.
- Should I prioritize latency or quality?
- It depends on your use case. Customer-facing applications benefit from faster models like Grok 4 Fast (5.8s average), while high-stakes applications may justify slower, more accurate models like GPT-5.1 (16.2s average). Many production systems find the best balance with mid-tier models around 8-12s latency.
- Why use ELO scoring for ranking?
- ELO scoring measures how often one model produces better RAG responses than another in direct comparisons. It reflects real-world consistency better than isolated metrics — a higher ELO means the model more reliably generates high-quality, well-grounded responses across diverse queries.
- Which datasets are used for evaluation?
- We benchmark LLMs on three datasets — MSMARCO (web search queries), PG (long-form essay content), and SciFact (scientific fact verification). This diversity ensures models are tested on different retrieval patterns and content types typical in production RAG systems.
- Should I use an open-source or proprietary LLM?
- Open-source models like DeepSeek R1 and GLM 4.6 offer cost advantages and deployment flexibility for self-hosting. Proprietary options like GPT-5.1 and Claude Opus 4.5 typically deliver better RAG quality with managed infrastructure. Choose based on your accuracy requirements, budget, and deployment constraints.