RAG guide

Best AI models for RAG

This page ranks models that fit document-backed assistants and retrieval pipelines, with extra weight on tool support, structured output, and enough context to hold retrieved evidence.

Live signalTool support for retrieval and workflow integration

Live signalStructured output support for controlled downstream handling

Live signalEnough context to hold retrieved chunks and instructions

Live signalLive pricing for document-backed production traffic

Fast answer

Start with the live shortlist, then validate the route

Most RAG teams should compare tool support, context room, and structured output before raw benchmark scores, because those factors determine whether the model fits the actual retrieval stack.

Data source and freshness

Catalog-backed, not a static price sheet

TVP refreshes this page from the live OpenRouter model catalog. This render used 428 public model records and was synchronized Jul 14, 2026, 9:34 PM UTC. Pricing, availability, and context values can change.

Verify the exact route before sending production traffic, then use the linked model and provider pages as the source for current values. Read the TVP data methodology.

Shortlist

Top live candidates right now

meta-llama

Meta: Llama 4 Scout

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...

Context10,000,000

Input$0.1

Output$0.3

10,000,000 token context, tool support, and structured output make it a better RAG candidate.

text
image
tools
structured

Model details Provider page

x-ai

xAI: Grok 4.20

Grok 4.20 is a reasoning model from xAI with industry-leading speed and agentic tool calling capabilities. It combines the lowest hallucination rate on the market with strict prompt adherance, delivering...

Context2,000,000

Input$1.25

Output$2.50

2,000,000 token context, tool support, and structured output make it a better RAG candidate.

text
image
file
tools
structured

Model details Provider page

openai

OpenAI: GPT-5.6 Luna

GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for...

Context1,050,000

Input$1.00

Output$6.00

1,050,000 token context, tool support, and structured output make it a better RAG candidate.

file
image
text
tools
structured

Model details Provider page

openai

OpenAI: GPT-5.6 Luna Pro

GPT-5.6 Luna Pro is the same underlying model as [GPT-5.6 Luna](TVP catalog), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

Context1,050,000

Input$1.00

Output$6.00

1,050,000 token context, tool support, and structured output make it a better RAG candidate.

file
image
text
tools
structured

Model details Provider page

openai

OpenAI: GPT-5.4

GPT-5.4 is OpenAI’s latest frontier model, unifying the Codex and GPT lines into a single system. It features a 1M+ token context window (922K input, 128K output) with support for...

Context1,050,000

Input$2.50

Output$15.00

1,050,000 token context, tool support, and structured output make it a better RAG candidate.

text
image
file
tools
structured

Model details Provider page

FAQ

What buyers usually ask

What should RAG teams compare first?

Most RAG teams should compare tool support, context room, and structured output before raw benchmark scores, because those factors determine whether the model fits the actual retrieval stack.

Why does pricing matter in RAG?

RAG prompts are often large because they include retrieved context. That makes per-token cost a bigger operational factor than in short chat routes.

Next step

Use the guide, then validate the route in live TVP data.

TVP keeps the shortlist connected to the current catalog, provider coverage, and token pricing so buyers can move from research to routing without starting over.

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