Best models · Updated 2026

Best AI Models for Data Extraction & RAG

17 models for Data Extraction & RAG, ordered by context window — not by price. Two models at the same rate per million tokens are not the same cost for the same document.

Why this table is not ranked by price

Models do not tokenize text the same way, so one document is a different number of tokens on each of them — and a provider can change tokenizer between versions of the same model. That makes $/1M a rate, not a price you can put in rank order: the cheaper rate can cost more per document. The columns below carry each provider's published list rate so you can cost them against your own token counts. Where a rate is introductory or announced to change, the model's own page says so.

That is a refusal to publish one price ranking that holds for every reader — not a refusal to choose on cost. Once a workload is fixed, its documents, token counts and quality bar are known, and comparing rates for that one job is a different and legitimate exercise: it is what our use-case shortlists do, one workload at a time.

ModelContextordered by thisInput / 1Mnot comparableOutput / 1Mnot comparable
GPT-5.4
OpenAI
1.05M$2.50$15.00View →
GPT-5.5
OpenAI
1.05M$5.00$30.00View →
1.05M$1.25$4.25View →
1.05M$1.25$4.25View →
1.05M$0.10$0.20View →
1M$10.00$50.00View →
Claude Opus 5
Anthropic
1M$5.00$25.00View →
1M$3.00$15.00View →
1M$1.25$2.50View →
400K$0.75$4.50View →
GLM-5
z.ai
200K$1.00$3.20View →
Context window not publishedThese may still fit your build. Their providers do not publish a context window, so they cannot be placed in the order above and we will not invent a figure to place them.
Command R
Cohere
View →
FireLLaVA-13b
Fireworks AI
View →
$0.30$2.50View →
$1.25$10.00View →
$1.50$7.50View →
$1.50$7.50View →

Ordered by context window — not by price, and not by paid placement. On a retrieval build the context window is the constraint you hit first — it sets how much retrieved source text fits in one call before you start chunking around it. Every figure is the provider's own published number; a dash means the provider does not publish it, and we would rather leave the cell empty than estimate it.

FAQ

Questions about AI models for Data Extraction & RAG

Which AI model is best for Data Extraction & RAG?+

There is no single answer — it depends on your workload, your quality bar and your volume. GPT-5.4, GPT-5.5, Muse Spark 1.1 lead this table on context window. Shortlist on the spec that constrains your build, then cost the shortlist against your own token counts.

Is the cheapest AI model for Data Extraction & RAG the cheapest to run?+

Not reliably. Models do not tokenize text the same way, so the same document is a different number of tokens on each one — and a provider can change tokenizer between versions of the same model. A lower rate per million tokens can still cost more per document. Compare published rates against your own token counts before choosing on price.

Not sure which model fits your Data Extraction & RAG workflow?

We'll match a model to your volume, quality bar, and budget — with a real cost estimate. One free call.

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