Best LLM for RAG (Retrieval-Augmented Generation)
Ranked on long-context accuracy, groundedness, and input-token price — RAG is input-token-heavy by design.
Updated April 2026. Top 3 this month: DeepSeek: R1 0528, Tencent: Hunyuan A13B Instruct, DeepSeek: DeepSeek V3.
How we rank
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.
Pillars and weights: Long-context accuracy (50%) · MMLU (20%) · input price (30%). Our full methodology is published on the methodology page.
Top ranked models
| Rank | Model | Provider | Input $/1M | Output $/1M | Context |
|---|---|---|---|---|---|
| 1 | DeepSeek: R1 0528 | DeepSeek | $0.50 | $2.15 | 163,840 |
| 2 | Tencent: Hunyuan A13B Instruct | Tencent | $0.14 | $0.57 | 131,072 |
| 3 | DeepSeek: DeepSeek V3 | DeepSeek | $0.32 | $0.89 | 163,840 |
| 4 | Qwen: Qwen3.5 Plus 2026-02-15 | Qwen | $0.26 | $1.56 | 1,000,000 |
| 5 | Arcee AI: Trinity Large Preview | Arcee AI | $0.00 | $0.00 | 131,000 |
| 6 | MiniMax: MiniMax M2.1 | MiniMax | $0.29 | $0.95 | 196,608 |
| 7 | Qwen: Qwen3.5 397B A17B | Qwen | $0.39 | $2.34 | 262,144 |
| 8 | Xiaomi: MiMo-V2-Flash | Xiaomi | $0.09 | $0.29 | 262,144 |
| 9 | MiniMax: MiniMax-01 | MiniMax | $0.20 | $1.10 | 1,000,192 |
| 10 | Meta: Llama 3.3 70B Instruct | Meta | $0.12 | $0.38 | 131,072 |
Tips for rag (retrieval-augmented generation)
- A 1M+ token context window is usually overkill. Optimize retrieval quality first.
- Prompt caching matters: pin the system prompt and retrieved context into the cache tier if available.
- Use batch pricing for bulk backfills over your corpus.
Frequently asked questions
Which LLM is best for RAG?
As of April 2026, our weighted top 3 are DeepSeek: R1 0528, Tencent: Hunyuan A13B Instruct, DeepSeek: DeepSeek V3.
Do I need a model with a 1M+ token context?
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.
Does cached input pricing help?
A lot. If your retrieved context has repeating chunks — documentation, policy, FAQs — cached-input pricing can cut your bill by 70–80%.
Does reasoning mode improve RAG quality?
For ambiguous queries, yes. For lookup-style queries, it just adds cost without improving grounding.
Related tasks
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Data refreshed daily via our snapshot cron. See our public JSON API for programmatic access.