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-rated search-augmented answers, as it ranks first among six models tested in head-to-head human preference voting on search tasks. DeepSeek: DeepSeek V4 Flash 0423 is the next-ranked alternative. Use for cost-sensitive RAG deployments where you need acceptable accuracy with few hallucinations, as it has been validated in production agent systems with dynamic intent detection and sub-agent orchestration.
About this recommendation
- Updated
- Sep 4, 2026
- Evidence through
- Sep 4, 2026
- Sources
- 5
- Revision
- v58
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 4
Anthropic
Sources evaluated
The task sets these weights before any model is scored.
| Evaluation feed | Weight | Winner result | Field measured |
|---|---|---|---|
| FACTS Groundingunavailable | 35% | feed unavailable | 0/20 |
| CRAGunavailable | 25% | feed unavailable | 0/20 |
| LMArena Search | 20% | #1 | 3/20 |
| LongBench v2unavailable | 10% | feed unavailable | 0/20 |
| OpenRouter usage | 10% | 97/100 | 20/20 |
Provider concentration
Each exact model is scored separately; provider identity is not a ranking input.
- Anthropic1 model
- deepseek1 model
- OpenAI1 model
- Z.ai1 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.
| Rank | Model | Relative score | Coverage | Practitioner evidence | Strongest measured reason |
|---|---|---|---|---|---|
| 01 | GPT-5.6 SolOpenAI | 55 | 30% | no linked practitioner threads | #1 LMArena Search |
| 02 | DeepSeek V4 Flash 0423deepseek | 53 | 10% | 2 threads · 2 families · 0 cautions | OpenRouter usage 99/100 normalized |
| 03 | GLM 5.2Z.ai | 44 | 10% | no linked practitioner threads | OpenRouter usage 97/100 normalized |
| 04 | Claude Fable 5Anthropic | 42 | 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.
Leads the LMArena search arena, indicating strong performance at producing answers with search integration.
Best when: Use when you need the highest-rated search-augmented answers, as it ranks first among six models tested in head-to-head human preference voting on search tasks.
Tips
- Use when you need the highest-rated search-augmented answers, as it ranks first among six models tested in head-to-head human preference voting on search tasks.
Deployed in production RAG pipelines with demonstrated low hallucination rates and acceptable information retrieval at minimal cost.
Best when: Use for cost-sensitive RAG deployments where you need acceptable accuracy with few hallucinations, as it has been validated in production agent systems with dynamic intent detection and sub-agent orchestration.
Tips
- Use for cost-sensitive RAG deployments where you need acceptable accuracy with few hallucinations, as it has been validated in production agent systems with dynamic intent detection and sub-agent orchestration.
- Use for document compilation workflows requiring parallel map/reduce over 30–50 document gates, as it has been proven in progressive UAT through full-corpus processing with web-viewable output.
Ranks mid-pack in overall text arena preferences, with no direct evidence for search or RAG-specific performance.
Best when: Consider only after reviewing the cited caution.
Watch out for
- Avoid assuming strong RAG performance based on general text arena ranking alone, as the available evidence does not cover search-augmented generation or citation accuracy.
Ranks third in LMArena's search arena, showing solid capability for search-augmented answer generation.
Best when: Use when you need search-augmented answers from a model with strong human preference ratings, ranking third of six in head-to-head search arena voting.
Tips
- Use when you need search-augmented answers from a model with strong human preference ratings, ranking third of six in head-to-head search arena voting.
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-rated search-augmented answers, as it ranks first among six models tested in head-to-head human preference voting on search tasks.[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 deployments where you need acceptable accuracy with few hallucinations, as it has been validated in production agent systems with dynamic intent detection and sub-agent orchestration.[2]
Sources
- 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
“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
“## 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 - 4
“Ranks #23 of 144 on LMArena's overall text arena (Elo 1472), based on blind human preference votes.”
LMArena text arena · Benchmark · Sep 1, 2026 - 5
“Ranks #3 of 6 on LMArena's search arena (score 1230), measuring answers produced with search.”
LMArena search arena · Benchmark · Aug 24, 2026
Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.