Task ranking

Best LLM for RAG & Long Context

Gemini 4 Argon ranks first for RAG & Long Context as of October 20, 2026.[1][2][3][4][5] It leads on human preference for long queries, overall text quality, and factual accuracy in blind comparisons. Watch out: Verify grounding on supplied source material explicitly, as its 78.6% on FACTS Grounding (#6 of 44) trails several competitors on this strict citation benchmark. GPT-6.1 Sol is the next-ranked alternative. It scores 82.4% on FACTS Grounding (#2 of 44), measuring factual support against supplied source material.

About this recommendation

Updated
Evidence through
Sources
14
Revision
v110

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

52%

intended feed weight

Largest provider share

4 of 8

Google

Established source breadth. 3 citation families and 0 practitioner families provide attributed capability reports; 0 cautionary threads retained. The largest citation family contributes 59%.

Google: Gemini 4 Argon leads the measured task comparison with 2 independent evaluation families. Community volume, release age and usage popularity add no ranking points. This category also weights its stated price or context criteria.

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.

winner: Gemini 4 Argon
Evaluation feedWeightWinner resultField measured
LongBench v2disabled; last successful check unknownunavailable
0%
feed unavailable0/24
FACTS GroundingObserved 2026-10-12 · evaluated 2026-10-12 · Gemini 4 Argon
25%
#624/24
context length
19%
52/10024/24
LMArena Document
19%
not measured9/24
LMArena Long QueryObserved 2026-10-15 · evaluation date not supplied · high effort
13%
#124/24
LMArena Search
13%
not measured4/24
LMArena Text FactualityObserved 2026-10-15 · evaluation date not supplied · high effort
13%
#121/24

Provider concentration

Each exact model is scored separately; provider identity is not a ranking input.

Google50%
  • Google4 models
  • OpenAI3 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.

  1. 01Gemini 4 Argon84

    Google · coverage 52%

    #1 LMArena Long Query · #1 LMArena Text Factuality

  2. 02GPT-6.1 Sol80

    OpenAI · coverage 62%

    #2 FACTS Grounding · #5 LMArena Search

  3. 03Gemini 3.7 Flash76

    Google · coverage 52%

    #9 FACTS Grounding · #12 LMArena Long Query

  4. 04GPT-6 Astra75

    OpenAI · coverage 76%

    #3 FACTS Grounding · #4 LMArena Search

  5. 05Gemini 3.8 Flash75

    Google · coverage 52%

    #7 LMArena Long Query · #13 FACTS Grounding

  6. 06Claude Opus 5.574

    Anthropic · coverage 62%

    #2 LMArena Long Query · #2 LMArena Text Factuality

  7. 07Gemini 3.5 Flash69

    Google · coverage 67%

    #12 FACTS Grounding · #14 LMArena Text Factuality

  8. 08GPT-5.6 Terra68

    OpenAI · coverage 67%

    #10 LMArena Document · #15 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.

  1. 01

    Gemini 4 ArgonGoogle · $2 in / $10 out per M tokens · 1.0M context

    Gemini 4 Argon leads on human preference for long queries, overall text quality, and factual accuracy in blind comparisons.

    Best when: Use for long-document RAG where human judges preferred its outputs on extended prompts, scoring Elo 1546 (#1 of 152) on LMArena's long-query category.
    Source 1
    “Within the 152 catalog models matched to this evaluation: Ranks #1 of 152 on LMArena's long-query category (Elo 1546), based on blind human preference for longer prompts.”
    LMArena long-query categoryOpen original ↗

    More tips

    • Deploy when factual accuracy in open-ended generation matters, as it ranks #1 of 111 on LMArena's factuality view (Elo 1511) based on blind human judgment.
      Source 3
      “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 factualityOpen original ↗

    Watch out for

    • Verify grounding on supplied source material explicitly, as its 78.6% on FACTS Grounding (#6 of 44) trails several competitors on this strict citation benchmark.
      Source 4
      “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 GroundingOpen original ↗
  2. 02

    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.
    Source 5
    “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 GroundingOpen original ↗

    No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.

  3. 03

    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.
    Source 6
    “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 GroundingOpen original ↗

    No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.

  4. 04

    GPT-6 AstraOpenAI · $10 in / $50 out per M tokens · 1.1M context

    GPT-6 Astra scores 82% on FACTS Grounding (#3 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.
    Source 7
    “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 GroundingOpen original ↗
  5. 05

    Gemini 3.8 FlashGoogle · $0.83 in / $4.13 out per M tokens · 1.0M context

    Gemini 3.8 Flash delivers strong human preference scores across long queries, overall quality, and factuality, with moderate grounding performance.

    Best when: Use for cost-sensitive long-context RAG where it ranks #7 of 152 on LMArena's long-query category (Elo 1507).
    Source 8
    “Within the 152 catalog models matched to this evaluation: Ranks #7 of 152 on LMArena's long-query category (Elo 1507), based on blind human preference for longer prompts.”
    LMArena long-query categoryOpen original ↗

    More tips

    • Deploy when overall response quality matters, ranking #8 of 152 on LMArena's overall text arena (Elo 1496).
      Source 9
      “Within the 152 catalog models matched to this evaluation: Ranks #8 of 152 on LMArena's overall text arena (Elo 1496), based on blind human preference votes.”
      LMArena text arenaOpen original ↗

    Watch out for

    • Check grounding carefully on source material, as its 73.1% on FACTS Grounding (#13 of 44) shows room for improvement on explicit citation fidelity.
      Source 10
      “Within the 44 catalog models matched to this evaluation: Scores 73.1% on FACTS Grounding (#13 of 44), measuring factual support against supplied source material.”
      FACTS GroundingOpen original ↗
  6. 06

    Claude Opus 5.5Anthropic · $4 in / $20 out per M tokens · 1M context

    Claude Opus 5.5 places second on long-query and factuality human preference, and third overall, yet ranks in the bottom half on explicit source-grounding accuracy.

    Best when: Choose for long-context workflows where human evaluators ranked it #2 of 152 on LMArena's long-query category (Elo 1531).
    Source 11
    “Within the 152 catalog models matched to this evaluation: Ranks #2 of 152 on LMArena's long-query category (Elo 1531), based on blind human preference for longer prompts.”
    LMArena long-query categoryOpen original ↗

    More tips

    • Select when blind human preference for factual accuracy is the priority, as it ranks #2 of 111 on LMArena's factuality view (Elo 1509).
      Source 12
      “Within the 111 catalog models matched to this evaluation: Ranks #2 of 111 on LMArena's factuality text view (Elo 1509), where answers are judged for factual accuracy in blind comparison.”
      LMArena text factualityOpen original ↗

    No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.

  7. 07

    Gemini 3.5 FlashGoogle · $1.50 in / $9 out per M tokens · 1.0M context

    Gemini 3.5 Flash scores 73.2% on FACTS Grounding (#12 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.
    Source 13
    “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 GroundingOpen original ↗

    No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.

  8. 08

    GPT-5.6 TerraOpenAI · $2 in / $12 out per M tokens · 1.1M context

    GPT-5.6 Terra shows solid document-task performance and source-grounding with reported context window discrepancies in tooling.

    Best when: Use for document workflows where it ranks #10 of 34 on LMArena's document arena (score 1472).
    Source 14
    “Within the 34 catalog models matched to this evaluation: Ranks #10 of 34 on LMArena's document arena (score 1472), based on blind preference for document tasks.”
    LMArena document arenaOpen original ↗

    No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.

Frequently asked

What is the best model for RAG & Long Context?
Gemini 4 Argon ranks first as of October 20, 2026. Gemini 4 Argon leads on human preference for long queries, overall text quality, and factual accuracy in blind comparisons.[1][2][3][4]
What should I watch out for with Gemini 4 Argon?
Verify grounding on supplied source material explicitly, as its 78.6% on FACTS Grounding (#6 of 44) trails several competitors on this strict citation benchmark.[4]
What is the best alternative to Gemini 4 Argon?
GPT-6.1 Sol ranks second. GPT-6.1 Sol scores 82.4% on FACTS Grounding (#2 of 44), measuring factual support against supplied source material.[5]

Sources

  1. 1

    “Within the 152 catalog models matched to this evaluation: Ranks #1 of 152 on LMArena's long-query category (Elo 1546), based on blind human preference for longer prompts.”

    LMArena long-query category · Benchmark · Oct 15, 2026
  2. 2

    “Within the 152 catalog models matched to this evaluation: Ranks #1 of 152 on LMArena's overall text arena (Elo 1524), based on blind human preference votes.”

    LMArena text arena · Benchmark · Oct 15, 2026
  3. 3

    “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
  4. 4

    “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
  5. 5

    “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
  6. 6

    “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
  7. 7

    “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
  8. 8

    “Within the 152 catalog models matched to this evaluation: Ranks #7 of 152 on LMArena's long-query category (Elo 1507), based on blind human preference for longer prompts.”

    LMArena long-query category · Benchmark · Oct 15, 2026
  9. 9

    “Within the 152 catalog models matched to this evaluation: Ranks #8 of 152 on LMArena's overall text arena (Elo 1496), based on blind human preference votes.”

    LMArena text arena · Benchmark · Oct 15, 2026
  10. 10

    “Within the 44 catalog models matched to this evaluation: Scores 73.1% on FACTS Grounding (#13 of 44), measuring factual support against supplied source material.”

    FACTS Grounding · Benchmark · Sep 3, 2026
  11. 11

    “Within the 152 catalog models matched to this evaluation: Ranks #2 of 152 on LMArena's long-query category (Elo 1531), based on blind human preference for longer prompts.”

    LMArena long-query category · Benchmark · Oct 15, 2026
  12. 12

    “Within the 111 catalog models matched to this evaluation: Ranks #2 of 111 on LMArena's factuality text view (Elo 1509), where answers are judged for factual accuracy in blind comparison.”

    LMArena text factuality · Benchmark · Oct 15, 2026
  13. 13

    “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
  14. 14

    “Within the 34 catalog models matched to this evaluation: Ranks #10 of 34 on LMArena's document arena (score 1472), based on blind preference for document tasks.”

    LMArena document arena · Benchmark · Sep 13, 2026

Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.