# Best LLM for Grounded RAG

> Updated Oct 20, 2026 · evidence through Oct 20, 2026. Synthesized from cited community evidence and open benchmarks, not vendor marketing. Canonical page: https://modelmatchbook.com/best/rag-grounded

Gemini 4 Argon ranks first for Grounded RAG as of October 20, 2026. Leads on LMArena factuality and SimpleQA while ranking sixth on FACTS Grounding, showing strong performance across both human preference and verified factual accuracy benchmarks. Watch out: Verify citation alignment manually, as FACTS Grounding score of 78.6% (#6 of 44) trails top performers on source-faithfulness benchmarks. GPT-6 Astra is the next-ranked alternative. Achieves 82% on FACTS Grounding (#3 of 44) and 75.6% on SimpleQA Verified (#2 of 69), with strong search-grounded factuality rankings on LMArena. [1,2,3,4,5,6]

## 1. Gemini 4 Argon
https://modelmatchbook.com/models/google/gemini-4-argon-20261013

Leads on LMArena factuality and SimpleQA while ranking sixth on FACTS Grounding, showing strong performance across both human preference and verified factual accuracy benchmarks.

**Best when:** Use for high-stakes RAG where human judges prefer the output, as it ranks #1 on LMArena's factuality text view with Elo 1511. [1]

**More tips:**
- Deploy for short-form factual QA requiring verified answers, achieving 76% on SimpleQA Verified (#1 of 69). [2]

**Watch out for:**
- Verify citation alignment manually, as FACTS Grounding score of 78.6% (#6 of 44) trails top performers on source-faithfulness benchmarks. [3]

## 2. GPT-6 Astra
https://modelmatchbook.com/models/openai/gpt-6-astra-20260903

Achieves 82% on FACTS Grounding (#3 of 44) and 75.6% on SimpleQA Verified (#2 of 69), with strong search-grounded factuality rankings on LMArena.

**Best when:** Prioritize for source-grounded generation where benchmark scores matter, scoring 82% on FACTS Grounding (#3 of 44). [5]

**More tips:**
- Use for search-augmented workflows, ranking #2 of 13 on LMArena's search factuality view with Elo 1240. [4]

**Watch out for:**
- Expect lower human preference rankings on open-ended text, placing #23 on LMArena's overall text arena (Elo 1477) and #27 on factuality text view (Elo 1466). [8,9]

## 3. GPT-6.1 Sol
https://modelmatchbook.com/models/openai/gpt-6.1-sol-20260929

GPT-6.1 Sol scores 82.4% on FACTS Grounding (#2 of 44), measuring factual support against supplied source material.

**Best when:** Its strongest result for this task is on FACTS Grounding, which measures staying faithful to supplied source documents. [10]

## 4. Claude Fable 5.1
https://modelmatchbook.com/models/anthropic/claude-fable-5.1-20260831

Claude Fable 5.1 ranks #2 of 13 on LMArena's search arena (score 1246), measuring answers produced with search.

**Best when:** Its strongest result for this task is on LMArena Search, which measures answers grounded in live search. [11]

## 5. Gemma 4 26B A4B 
https://modelmatchbook.com/models/google/gemma-4-26b-a4b-it-20260403

Gemma 4 26B A4B  scores 80.9% on FACTS Grounding (#4 of 44), measuring factual support against supplied source material.

**Best when:** Its strongest result for this task is on FACTS Grounding, which measures staying faithful to supplied source documents. [7]

## 6. Gemini 3.7 Flash
https://modelmatchbook.com/models/google/gemini-3.7-flash-20260813

Gemini 3.7 Flash scores 75.5% on FACTS Grounding (#9 of 44), measuring factual support against supplied source material.

**Best when:** Its strongest result for this task is on FACTS Grounding, which measures staying faithful to supplied source documents. [12]

## 7. Gemini 3.8 Flash
https://modelmatchbook.com/models/google/gemini-3.8-flash-20260902

Shows severe multi-turn degradation in production RAG evaluations and document extraction inconsistencies, with mid-tier benchmark scores.

**Best when:** Consider for single-turn queries only, ranking #18 on LMArena's factuality text view (Elo 1474) before degradation occurs. [13]

**Watch out for:**
- Avoid for multi-turn conversations, as fact coverage collapsed from 89% to 10% across five rounds with knowledge base answers deviating to generic responses. [14]
- Audit document extraction outputs for duplicates with conflicting data, as observed with 92 product entries containing 18 duplicates with contradictory brand and specification fields. [15]

## 8. Gemini 3.5 Flash
https://modelmatchbook.com/models/google/gemini-3.5-flash-20260519

Mid-tier performance on FACTS Grounding and LMArena factuality with reported issues around grounding metadata in Vertex AI Search integrations.

**Best when:** Use for basic retrieval tasks where 73.2% FACTS Grounding (#12 of 44) suffices for the use case. [16]

**Watch out for:**
- Expect lower verified factual accuracy, scoring 66.2% on SimpleQA Verified (#12 of 69). [17]

## FAQ

**What is the best model for Grounded RAG?**

Gemini 4 Argon ranks first as of October 20, 2026. Leads on LMArena factuality and SimpleQA while ranking sixth on FACTS Grounding, showing strong performance across both human preference and verified factual accuracy benchmarks. [1,2,3]

**What should I watch out for with Gemini 4 Argon?**

Verify citation alignment manually, as FACTS Grounding score of 78.6% (#6 of 44) trails top performers on source-faithfulness benchmarks. [3]

**What is the best alternative to Gemini 4 Argon?**

GPT-6 Astra ranks second. Achieves 82% on FACTS Grounding (#3 of 44) and 75.6% on SimpleQA Verified (#2 of 69), with strong search-grounded factuality rankings on LMArena. [4,5,6]

**Which open-weight model ranks highest for Grounded RAG?**

Gemma 4 26B A4B  is the highest-ranked open-weight model, at #5. Gemma 4 26B A4B  scores 80.9% on FACTS Grounding (#4 of 44), measuring factual support against supplied source material. [7]

## Sources
1. "Within the 111 catalog models matched to this evaluation: Ranks #1 of 111 on LMArena's factuality text view (Elo 1511), where answers are judged for factual accuracy in blind comparison." (LMArena text factuality, https://lmarena.ai/leaderboard)
2. "Within the 69 catalog models matched to this evaluation: Answers 76% of SimpleQA Verified correctly in Epoch AI's independent run (#1 of 69), short factual questions with a single verified answer." (SimpleQA Verified (Epoch AI), https://epoch.ai/benchmarks)
3. "Within the 44 catalog models matched to this evaluation: Scores 78.6% on FACTS Grounding (#6 of 44), measuring factual support against supplied source material." (FACTS Grounding, https://www.kaggle.com/benchmarks/google/facts-grounding)
4. "Within the 13 catalog models matched to this evaluation: Ranks #2 of 13 on LMArena's search factuality view (Elo 1240), measuring the factual accuracy of search-grounded answers." (LMArena search factuality, https://lmarena.ai/leaderboard)
5. "Within the 44 catalog models matched to this evaluation: Scores 82% on FACTS Grounding (#3 of 44), measuring factual support against supplied source material." (FACTS Grounding, https://www.kaggle.com/benchmarks/google/facts-grounding)
6. "Within the 69 catalog models matched to this evaluation: Answers 75.6% of SimpleQA Verified correctly in Epoch AI's independent run (#2 of 69), short factual questions with a single verified answer." (SimpleQA Verified (Epoch AI), https://epoch.ai/benchmarks)
7. "Within the 44 catalog models matched to this evaluation: Scores 80.9% on FACTS Grounding (#4 of 44), measuring factual support against supplied source material." (FACTS Grounding, https://www.kaggle.com/benchmarks/google/facts-grounding)
8. "Within the 152 catalog models matched to this evaluation: Ranks #23 of 152 on LMArena's overall text arena (Elo 1477), based on blind human preference votes." (LMArena text arena, https://lmarena.ai/leaderboard)
9. "Within the 111 catalog models matched to this evaluation: Ranks #27 of 111 on LMArena's factuality text view (Elo 1466), where answers are judged for factual accuracy in blind comparison." (LMArena text factuality, https://lmarena.ai/leaderboard)
10. "Within the 44 catalog models matched to this evaluation: Scores 82.4% on FACTS Grounding (#2 of 44), measuring factual support against supplied source material." (FACTS Grounding, https://www.kaggle.com/benchmarks/google/facts-grounding)
11. "Within the 13 catalog models matched to this evaluation: Ranks #2 of 13 on LMArena's search arena (score 1246), measuring answers produced with search." (LMArena search arena, https://lmarena.ai/leaderboard)
12. "Within the 44 catalog models matched to this evaluation: Scores 75.5% on FACTS Grounding (#9 of 44), measuring factual support against supplied source material." (FACTS Grounding, https://www.kaggle.com/benchmarks/google/facts-grounding)
13. "Within the 111 catalog models matched to this evaluation: Ranks #18 of 111 on LMArena's factuality text view (Elo 1474), where answers are judged for factual accuracy in blind comparison." (LMArena text factuality, https://lmarena.ai/leaderboard)
14. "現象 同一份 60 題題組跑三輪、五個模型，只有 Google 供應商的答案品質隨輪次崩潰： | 模型 | 第1輪 | 第2輪 | 第3輪 | 第4輪 | 第5輪 | |---|---|---|---|---|---| | gemini-3.8-flash | 89% | 36% | 18% | 10% | 17% | | gpt-5.6-luna | 77% | 82% | 100% | 83% | 100% | | gpt-5.6-terra | 89% | 82% | 100% | 81% | 100% | | qwen3.6:35b-a3b-q8_0 | 81% | 79% | 97% | 81% | 100% | | qwen3.8:27b-q8_0 | 89% | 82% | 95% | 83% | 100% | （數字＝金標準事實覆蓋率，三輪合計 180 筆/臂） 第二輪起 Gemini 的回答脫離知識庫，改用通用常識作答，甚至明說不知道品牌： > 問：客服電話是多少？ > 答：抱歉，由於我目前**沒有您所在…" (larry610881, https://github.com/larry610881/agentic-rag-customer-service/issues/87)
15. "現象 評測租戶知識庫「DM」的 DM_家樂福_采集漫旅_商品頁_p11-15.pdf（5 頁）以 Gemini 3.8 Flash 走混合 OCR（切片 2x3 + 整頁）處理後，落庫內容出現： - 16 組 【頁面標題】（其中 10 組為「不詳」），但原始 PDF 只有 5 頁 - 92 筆 商品： 條目，只有 74 筆唯一 — 18 筆重複 - 重複條目的欄位互相矛盾，例如同一商品「鮮剖100%椰子汁」出現兩次： - 一處 品牌：半天水、規格：含椰肉/純天然 520毫升×3、原價：120~129 - 另一處 品牌：鮮剖、規格：520毫升×3、原價：120-129元、備註：含椰肉/純天然 - 前者所在區段有完整頁面標題「單筆購買第12~15頁…」，後者所在區段頁面標題為「不詳」 已排除 chunk overlap 造成的假重複：把 chunk 依 chunk_index 串接後長度等於各 chunk 長度總和（無重疊），上述統計是文件本體的真實內容。 也已排除 worker 跑舊版：VM `poc-rag-v…" (larry610881, https://github.com/larry610881/agentic-rag-customer-service/issues/83)
16. "Within the 44 catalog models matched to this evaluation: Scores 73.2% on FACTS Grounding (#12 of 44), measuring factual support against supplied source material." (FACTS Grounding, https://www.kaggle.com/benchmarks/google/facts-grounding)
17. "Within the 69 catalog models matched to this evaluation: Answers 66.2% of SimpleQA Verified correctly in Epoch AI's independent run (#12 of 69), short factual questions with a single verified answer." (SimpleQA Verified (Epoch AI), https://epoch.ai/benchmarks)