Best for: RAG (Retrieval-Augmented Generation)
Best LLM for RAG (Retrieval-Augmented Generation)
Ranked on long-context accuracy, groundedness, and input-token price — RAG is input-token-heavy by design.
Podium
This month’s top three.
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How we rank
Weights tuned for rag (retrieval-augmented generation).
RAG workloads push enormous amounts of retrieved context through a model. The three things that matter: does it faithfully use what you retrieved (groundedness), does it degrade when the context is long (needle-in-a-haystack), and how much will a million input tokens cost you. Because RAG is input-heavy, the input price pillar gets a heavier weight than it does for agentic or generative workloads.
Our full methodology is published on the methodology page.
Pillars and weights:
- Long-context accuracy50%
- MMLU20%
- input price30%
Full ranking
Top ranked models
No models currently match this task's filter. The admin team adds more models regularly — check back soon.
Field notes
Tips for rag (retrieval-augmented generation)
- 01
A 1M+ token context window is usually overkill. Optimize retrieval quality first.
- 02
Prompt caching matters: pin the system prompt and retrieved context into the cache tier if available.
- 03
Use batch pricing for bulk backfills over your corpus.
FAQ
Frequently asked questions
The questions teams ask before picking a model for rag (retrieval-augmented generation).
Get instant answers from our AI agent
As of September 2026, our weighted top 3 are the top frontier models.
Almost never. Most RAG systems send 10–50k tokens per query. A 200k context is plenty; a 1M context is a nice-to-have for edge cases.
A lot. If your retrieved context has repeating chunks — documentation, policy, FAQs — cached-input pricing can cut your bill by 70–80%.
For ambiguous queries, yes. For lookup-style queries, it just adds cost without improving grounding.