# Best AI model for RAG — September 2026

> Best AI model for RAG (September 2026): on BenchLM's evidence, GPT-6 Astra has the highest reasoning score estimate (89.5) among models that meet the page's constraints, followed by Claude Fable 5.1 and Kimi K3. Evidence, constraints, and caveats included.

## Constraints on this page

A builder choosing by accuracy, hosted processing allowed, any price, ordinary input size. Every refine link below changes one answer.

## The shortlist for RAG

| # | Model | Creator | Reasoning score estimate | Why |
|---|---|---|---|---|
| 1 | [GPT-6 Astra](/models/gpt-6-astra) | OpenAI | 89.5 | Highest reasoning estimate among models that meet every stated constraint; $20.00 for the stated workload; 1.05M context |
| 2 | [Claude Fable 5.1](/models/claude-fable-5-1) | Anthropic | 79.4 | Estimate 79.4 on the same reasoning evidence; $20.00 for the stated workload; 1M context |
| 3 | [Kimi K3](/models/kimi-k3) | Moonshot AI | 78.5 | Estimate 78.5 on the same reasoning evidence; $6.00 for the stated workload; 1.05M context |
| 4 | [MiniMax M3](/models/minimax-m3) | MiniMax | 78.0 | Estimate 78.0 on the same reasoning evidence; $0.54 for the stated workload; 1M context; Open weights, so it can run on your own hardware |
| 5 | [Muse Spark 1.3](/models/muse-spark-1-3) | Meta | 78.0 | Estimate 78.0 on the same reasoning evidence; $2.10 for the stated workload; 1M context |

Ordered by task score under the stated constraints.

## What this shortlist rests on

- 25% · [ARC-AGI-2](/benchmarks/arc-agi-2) · Current
- 25% · [LongBench v2](/benchmarks/longbench-v2) · Current
- 20% · [MRCRv2](/benchmarks/mrcrv2) · Current
- 15% · [AA-LCR](/benchmarks/lcr) · Current
- 15% · [ARC-AGI-3](/benchmarks/arcagi3) · Current

## What to verify before choosing

- Long context is not retrieval quality. Test retrieval, grounding, permissions, and citations separately.
- Composite scores are estimates. A small score gap does not establish a reliably better model.

## Refine

- [Change any answer in the selector](/tools/llm-selector?audience=builders&family=automation&useCase=rag&priority=accuracy&privacy=hosted&budget=any&context=normal)
- [Must run on my own hardware](/tools/llm-selector?audience=builders&family=automation&useCase=rag&priority=accuracy&privacy=local&context=normal)
- [Cost matters most](/tools/llm-selector?audience=builders&family=automation&useCase=rag&priority=cost&privacy=hosted&budget=any&context=normal)
- [Speed matters most](/tools/llm-selector?audience=builders&family=automation&useCase=rag&priority=speed&privacy=hosted&budget=any&context=normal)
- [Long documents or a large codebase](/tools/llm-selector?audience=builders&family=automation&useCase=rag&priority=accuracy&privacy=hosted&budget=any&context=large)
- [Choosing an everyday assistant, not an API](/tools/llm-selector?audience=everyday&family=automation&useCase=rag&priority=accuracy&privacy=hosted&budget=any&context=normal)
- [Describe your own job instead](/tools/llm-selector)

## Questions

**Which AI model is best for RAG?**

On BenchLM's public evidence, GPT-6 Astra by OpenAI has the highest reasoning score estimate among models that meet the page's default constraints (89.5). Ordered by task score under the stated constraints. Long context is not retrieval quality. Test retrieval, grounding, permissions, and citations separately.

**What are the alternatives to GPT-6 Astra for RAG?**

Claude Fable 5.1 (79.4), Kimi K3 (78.5), MiniMax M3 (78.0), Muse Spark 1.3 (78.0) follow on the same evidence. A small gap does not establish a reliably better model; compare them on three representative examples of your own work.

**How does BenchLM pick the best ai model for rag?**

The page runs the LLM Selector with fixed answers: a builder choosing by accuracy, hosted processing allowed, any price, ordinary input size. The selector uses the reasoning evidence surface, filters by the stated constraints, and orders by that evidence. It never adds a hidden fit score or a bonus for open weights or reasoning style.

**Can I change the constraints?**

Yes. Every link under "Refine" opens the selector with one answer changed, and the address carries the answers so a result can be shared or reopened against the current dataset.

Data updated 2026-09-18. Method: bench-align-v5.5-2026-09-04.

Canonical page: https://benchlm.ai/best/for/rag