Recommendation for Grounded QA

Grounded RAG

Our top recommendation for Grounded RAG, based on the public evidence we track, is OpenAI: GPT-5.6 Sol.[1][2] Use when you need the highest-ranked search-augmented answers among tested models, as it scored 1257 on LMArena's search benchmark. DeepSeek: DeepSeek V4 Flash 0423 is the next-ranked alternative. Use for cost-sensitive RAG workflows where you need acceptable accuracy with minimal hallucinations, as reported in agent and sub-agent deployments.

About this recommendation

Updated
Sep 25, 2026
Evidence through
Sep 25, 2026
Sources
3
Revision
v79

Decision audit

Why this result

Inspect the inputs and the computed order behind the recommendation.

Models screened

20

live candidates

Evaluation feeds

5

task-weighted

Winner coverage

30%

intended feed weight

Largest provider share

1 of 3

Anthropic

Provisional source breadth. 3 citation families and 0 practitioner families support the top result; 0 cautionary threads is retained. The largest citation family contributes 50%.

Sources evaluated

The task sets these weights before any model is scored.

winner: GPT-5.6 Sol
Evaluation feedWeightWinner resultField measured
FACTS Groundingunavailable
35%
feed unavailable0/20
CRAGunavailable
25%
feed unavailable0/20
LMArena Search
20%
#13/20
LongBench v2unavailable
10%
feed unavailable0/20
OpenRouter usage
10%
97/10020/20

Provider concentration

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

Anthropic33%
  • Anthropic1 model
  • deepseek1 model
  • OpenAI1 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.

RankModelRelative scoreCoveragePractitioner evidenceStrongest measured reason
01GPT-5.6 SolOpenAI
55
30%no linked practitioner threads#1 LMArena Search
02DeepSeek V4 Flash 0423deepseek
53
10%2 threads · 2 families · 0 cautionsOpenRouter usage 98/100 normalized
03Claude Fable 5Anthropic
41
30%no linked practitioner threads#3 LMArena Search

Relative score combines normalized benchmark quality and signal coverage; independent practitioner evidence and freshness are bounded tie-breakers. It is an ordering score, not an absolute quality percentage. The writing model receives this order and cannot change it.

  1. Leads LMArena's search arena, indicating strong performance at producing answers grounded in retrieved information.

    Best when: Use when you need the highest-ranked search-augmented answers among tested models, as it scored 1257 on LMArena's search benchmark.

    Tips

    • Use when you need the highest-ranked search-augmented answers among tested models, as it scored 1257 on LMArena's search benchmark.
      Source 1
      “Ranks #1 of 6 on LMArena's search arena (score 1257), measuring answers produced with search.”
      LMArena search arenaOpen original ↗
  2. Deployed successfully in a production RAG pipeline compiling 30–50 document corpora into a visitable wiki, with reports of few hallucinations and acceptable information retrieval at low cost.

    Best when: Use for cost-sensitive RAG workflows where you need acceptable accuracy with minimal hallucinations, as reported in agent and sub-agent deployments.

    Tips

    • Use for cost-sensitive RAG workflows where you need acceptable accuracy with minimal hallucinations, as reported in agent and sub-agent deployments.
      Source 2
      “I've been extremely impressed with DeepSeek V4 flash. We've been working on a project which can be thought of as an agent, just not for coding. So we've been building everything: agents, sub-agents, RAG, dynamic intent detection, changing models based on what's being done, etc. In our tests, DeepSeek V4-flash is the cheapest model with acceptable replies (few hallucinations, while finding the right information). It's not the cheapest one we run overall (we're actually surviving with 3B models f…”
    • Use for document compilation pipelines processing tens of documents through map/reduce stages, proven in a production wiki generation system.
      Source 3
      “## Destination Fork personal-mode compile loop re-plumbed to run through **pi** as its LLM backend (A段: pi as stateless executor, serial; B段: orchestration moves inside pi as a TS workflow via the workflow extension, 2-lane parallel map/reduce), with the map/reduce prompts ported to **deepseek/deepseek-v4-flash**. Proven by progressive UAT: UAT-2 sample (~10-doc smoke → 30–50-doc gate) then UAT-1 full corpus, ending in a **visitable wiki** (web view + Q&A over compiled output). Execution is car…”
      proletariat64Open original ↗
  3. Anthropic: Claude Fable 5 ranks #3 of 6 on LMArena's search arena (score 1230), measuring answers produced with search.

    Best when: Consider only after reviewing the cited caution.

Frequently asked

What is the top-ranked model for Grounded RAG?
OpenAI: GPT-5.6 Sol ranks first in the current evidence-weighted comparison. Use when you need the highest-ranked search-augmented answers among tested models, as it scored 1257 on LMArena's search benchmark.[1]
What is an alternative to OpenAI: GPT-5.6 Sol?
DeepSeek: DeepSeek V4 Flash 0423 is the next-ranked option. Use for cost-sensitive RAG workflows where you need acceptable accuracy with minimal hallucinations, as reported in agent and sub-agent deployments.[2]

Sources

  1. 1

    “Ranks #1 of 6 on LMArena's search arena (score 1257), measuring answers produced with search.”

    LMArena search arena · Benchmark · Aug 24, 2026
  2. 2

    “I've been extremely impressed with DeepSeek V4 flash. We've been working on a project which can be thought of as an agent, just not for coding. So we've been building everything: agents, sub-agents, RAG, dynamic intent detection, changing models based on what's being done, etc. In our tests, DeepSeek V4-flash is the cheapest model with acceptable replies (few hallucinations, while finding the right information). It's not the cheapest one we run overall (we're actually surviving with 3B models f…”

    jorl17 · Hacker News · May 22, 2026
  3. 3

    “## Destination Fork personal-mode compile loop re-plumbed to run through **pi** as its LLM backend (A段: pi as stateless executor, serial; B段: orchestration moves inside pi as a TS workflow via the workflow extension, 2-lane parallel map/reduce), with the map/reduce prompts ported to **deepseek/deepseek-v4-flash**. Proven by progressive UAT: UAT-2 sample (~10-doc smoke → 30–50-doc gate) then UAT-1 full corpus, ending in a **visitable wiki** (web view + Q&A over compiled output). Execution is car…”

    proletariat64 · GitHub · Aug 16, 2026

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