Best LLM for Document Q&A / RAG in 2026
Answering questions over retrieved chunks. Best with native citation models. Below is the current ranked list, based on benchmark scores and capability weights specific to this use case. Each entry includes the model's score, list price, and a one-line "why it ranks here" note.
Ranked list
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Command R+
— Cohere
Score 91.0
$2.50/$10.00 per M
128K ctx
Native citations for grounded answers. Built for RAG.
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Claude Sonnet 4.6
— Anthropic
Score 89.0
$3.00/$15.00 per M
200K ctx
Best general-purpose model for RAG when citations are added via prompting.
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Gemini 2.5 Flash
— Google
Score 86.0
$0.30/$2.50 per M
1.0M ctx
Cheap and good. Best when chunks are large.
Selection criteria
Rankings weight the following factors for this use case:
- reasoning: 30%
- truthfulness: 40%
- price: 30%
Weights reflect what matters for this workload — for example, "code generation" weights coding benchmarks heavily and price moderately, while "customer support" weights price and latency more than peak quality. Reasonable people will weight differently; the cost calculator and comparison tool let you reproduce the math with your own assumptions.
What this use case actually involves
Answering questions over retrieved chunks. Best with native citation models. Real-world implementations of this workload typically involve a mix of model calls, retrieval, and post-processing. The ranking above is for the model-call portion in isolation; total cost and latency depend on the surrounding architecture.
How the ranking is built
Composite scores are derived from the listed benchmark scores weighted by the factors above, plus capability fit (does the model support tool use, vision, function calling, etc.). The result is not a single "best model" answer — it's an ordered list with a clear rationale for each rank, so you can override based on requirements the ranking can't model (procurement constraints, regional availability, data residency).