Part of Best LLM for RAG & Long Context
Best LLM for Grounded RAG
Gemini 4 Argon ranks first for Grounded RAG as of October 20, 2026.[1][2][3][4][5][6] 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.
- Best non-Google pick: GPT-6 Astra (#2)
- Best open-weight pick: Gemma 4 26B A4B (#5)
- Compare Gemini 4 Argon vs GPT-6 Astra →
About this recommendation
- Updated
- Evidence through
- Sources
- 17
- Revision
- v108
Decision audit
Why this result
Inspect the inputs and the computed order behind the recommendation.
Models screened
24
live candidates
Evaluation feeds
7
task-weighted
Winner coverage
62%
intended feed weight
Largest provider share
5 of 8
Google: Gemini 4 Argon leads the measured task comparison with 3 independent evaluation families. Community volume, release age and usage popularity add no ranking points.
Real-world track record is still developing. The recommendation can lead on independent measurements while practitioner reports accumulate.
Sources evaluated
These are the weights used in scoring. Missing measurements are omitted from each model’s average. A feed with no result for any model released in the last 60 days is stale and does not score until it catches up.
| Evaluation feed | Weight | Winner result | Field measured |
|---|---|---|---|
| FACTS GroundingObserved 2026-10-12 · evaluated 2026-10-12 · Gemini 4 Argon | 41% | #6 | 16/24 |
| SimpleQA Verified (Epoch AI)Observed 2026-10-13 · evaluated 2026-10-13 · high effort · standard error 1.40 | 18% | #1 | 20/24 |
| LMArena Text FactualityObserved 2026-10-15 · evaluation date not supplied · high effort | 18% | #1 | 24/24 |
| CRAGdisabled; last successful check unknownunavailable | 0% | feed unavailable | 0/24 |
| LMArena Search | 12% | not measured | 5/24 |
| LMArena Search Factuality | 12% | not measured | 5/24 |
| LongBench v2disabled; last successful check unknownunavailable | 0% | feed unavailable | 0/24 |
Provider concentration
Each exact model is scored separately; provider identity is not a ranking input.
- Google5 models
- OpenAI2 models
- Anthropic1 model
Decision table
Every published model is shown in computed order. Practitioner sources are distinct community threads, not the citations repeated in the prose below.
Google · coverage 62%
#1 SimpleQA Verified (Epoch AI) · #1 LMArena Text Factuality
OpenAI · coverage 81%
#2 SimpleQA Verified (Epoch AI) · #2 LMArena Search Factuality
OpenAI · coverage 81%
#2 FACTS Grounding · #3 SimpleQA Verified (Epoch AI)
Anthropic · coverage 48%
#2 LMArena Search · #3 LMArena Text Factuality
Google · coverage 48%
#4 FACTS Grounding · #58 LMArena Text Factuality
Google · coverage 62%
#9 FACTS Grounding · #10 SimpleQA Verified (Epoch AI)
Google · coverage 62%
#8 SimpleQA Verified (Epoch AI) · #13 FACTS Grounding
Google · coverage 62%
#12 SimpleQA Verified (Epoch AI) · #12 FACTS Grounding
Task score averages the available, task-weighted measurements. Benchmark positions compare models matched to our catalog. Models measured by at least two independent evaluator families are considered first. Missing tests, practitioner report counts, popularity and release age add no points. Coverage describes evidence breadth, not a probability of correctness. Scores within two points are shown as close under this policy; this is not a statistical confidence interval. When the leading scores are that close, the model measured by more evaluator families ranks first.
- 01
Gemini 4 ArgonGoogle · $2 in / $10 out per M tokens · 1.0M context
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.More tips
- Deploy for short-form factual QA requiring verified answers, achieving 76% on SimpleQA Verified (#1 of 69).
Watch out for
- Verify citation alignment manually, as FACTS Grounding score of 78.6% (#6 of 44) trails top performers on source-faithfulness benchmarks.
- 02
GPT-6 AstraOpenAI · $10 in / $50 out per M tokens · 1.1M context
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).More tips
- Use for search-augmented workflows, ranking #2 of 13 on LMArena's search factuality view with Elo 1240.
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).
- 03
GPT-6.1 SolOpenAI · $2 in / $10 out per M tokens · 1.1M context
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.No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.
- 04
Claude Fable 5.1Anthropic · $10 in / $50 out per M tokens · 1M context
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.No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.
- 05
Gemma 4 26B A4B Google · $0.090 in / $0.30 out per M tokens · 262K context · open-weight
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.No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.
- 06
Gemini 3.7 FlashGoogle · $0.75 in / $3.75 out per M tokens · 1.0M context
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. - 07
Gemini 3.8 FlashGoogle · $0.83 in / $4.13 out per M tokens · 1.0M context
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.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.
- Audit document extraction outputs for duplicates with conflicting data, as observed with 92 product entries containing 18 duplicates with contradictory brand and specification fields.
- 08
Gemini 3.5 FlashGoogle · $1.50 in / $9 out per M tokens · 1.0M context
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.Watch out for
- Expect lower verified factual accuracy, scoring 66.2% on SimpleQA Verified (#12 of 69).
Frequently asked
- 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 · Benchmark · Oct 15, 2026 - 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) · Benchmark · Oct 13, 2026 - 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 · Benchmark · Oct 12, 2026 - 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 · Benchmark · Oct 13, 2026 - 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 · Benchmark · Sep 11, 2026 - 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) · Benchmark · Aug 30, 2026 - 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 · Benchmark · Oct 20, 2026 - 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 · Benchmark · Oct 15, 2026 - 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 · Benchmark · Oct 15, 2026 - 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 · Benchmark · Oct 13, 2026 - 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 · Benchmark · Oct 13, 2026 - 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 · Benchmark · Aug 27, 2026 - 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 · Benchmark · Oct 15, 2026 - 14
fix(agent): 多輪對話送出兩個 SystemMessage，Gemini 只保留一個 → 檢索內容被丟棄，第二輪起答案脫離知識庫
Original-language excerpt:“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 · GitHub · Sep 8, 2026 - 15
fix(knowledge): 混合 OCR 產生重複頁面與矛盾商品條目（5 頁 PDF 出現 16 組頁面標題、92/74 商品重複）
Original-language excerpt:“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 · GitHub · Sep 8, 2026 - 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 · Benchmark · Oct 20, 2026 - 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) · Benchmark · Aug 27, 2026
Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.