Best LLM for Long Documents & RAG in 2026
For long documents & rag, Gemini 3.1 Pro is our pick: $2.07/M tokens on a Long-document Q&A workload, 55 tokens/sec, 2M context.
Feeding a whole document or codebase into a single call needs headroom well beyond the text itself — retrieval overhead, system prompts, and chat history all eat into the window. We require at least 200K tokens of context and rank primarily on window size.
What is the best LLM for long documents & rag?
Gemini 3.1 Pro, from Google, is the best fit for long documents & rag at $2.07 per million task tokens on a Long-document Q&A workload, measured at 55 tokens/sec, with a 2M-token context window. No cheaper value pick beats it for this task.
Can't use Gemini 3.1 Pro? See Gemini 3.1 Pro alternatives.
Evidence
Ranked — top 8 eligible models
"Fit" is a requirements match, not a quality benchmark — it combines price, measured speed, context window, and (where we have run it) graded accuracy on this task. Formula below.
| # | Model | Provider | Fit | Evidence | Task price/M | Tokens/sec | Context | Scored on |
|---|---|---|---|---|---|---|---|---|
| 1 | Gemini 3.1 Pro | 68 | — | $2.07 | 55 | 2M | price, context, speed | |
| 2 | Gemini 3.5 Flash Lite | 61 | — | $0.26 | 162 | 1M | price, context, speed | |
| 3 | GLM-5.2 | Z.ai | 61 | — | $1.42 | — | 1M | price, context |
| 4 | Grok 4.3 | xAI | 53 | — | $1.26 | 98 | 1M | price, context, speed |
| 5 | Gemini 3.6 Flash | 52 | — | $1.55 | 114 | 1M | price, context, speed | |
| 6 | Grok-4.20 | xAI | 50 | — | $2.03 | 104 | 1M | price, context, speed |
| 7 | Grok-4.20 Reasoning | xAI | 50 | — | $2.03 | 52 | 1M | price, context, speed |
| 8 | Claude Fable 5 | Anthropic | 42 | — | $10.26 | 41 | 1M | price, context, speed |
What this costs you
At 2,000 long-document q&a calls/month:
| Model | Task price/M | Est. monthly cost |
|---|---|---|
| Gemini 3.1 Pro | $2.07 | $624.00 |
| Gemini 3.5 Flash Lite | $0.26 | $78.00 |
| GLM-5.2 | $1.42 | $428.80 |
How we ranked this
Weights: evidence 0%, price 25%, speed 15%, context 60%.
Requirements: ≥200K context. 21 models eligible.
Price and context sub-scores are min-max normalised (log-scaled) within this task's eligible set only. Speed uses measured tokens/sec only — estimated rows are excluded. A model missing a measurement is never scored as zero: its weight is redistributed across the components we do have, and "Scored on" in the table above shows exactly which ones.
Prices verified 2026-08-08.
Related
FAQ
How much context headroom do I actually need?
Budget for your document plus retrieval overhead, system prompt, and conversation history — a 150K-token document comfortably needs a 200K+ window, not exactly 150K.
Does a bigger context window mean better recall inside it?
Not necessarily — window size is a hard capacity limit, not a quality guarantee. Very long prompts can still see recall degrade in the middle of the context ("lost in the middle").
Is RAG still worth it if the context window is huge?
Often yes — retrieval keeps cost and latency down by sending only relevant passages instead of the whole corpus, even when the model could technically fit everything.