LLM OCR Pricing Per 1,000 Pages by Model

Note the third column. Every one of these returns a per-word confidence score. No language model in the table above does, and that gap is the reason plenty of teams still route financial and legal documents through a classical engine even when a model is cheaper.

One consequence worth noting: Gemini charges a flat 258 tokens per page regardless of how big the page is, so its input cost barely moves, which is exactly why output ends up being 92 to 96 percent of a Gemini bill. OpenAI and Anthropic both scale input with page size until they hit a cap.

Frequently asked questions

How much does LLM OCR cost per 1,000 pages?

It ranges from about $0.33 to about $210 per 1,000 pages depending on the model. Gemini 2.5 Flash-Lite is roughly $0.33, GPT-6 Luna about $0.62, GPT-5.4-nano about $1.44, GPT-5.4-mini about $5.25, Claude Haiku 4.5 about $5.31, GPT-6 Sol about $12.49, Claude Sonnet 5 about $12.93, Claude Opus 5.5 about $25.86 and Claude Opus 5 about $32.32. Dedicated OCR from AWS, Azure and Google is a flat $1.50 for plain text.

Which LLM is cheapest for OCR?

Gemini 2.5 Flash-Lite, at roughly $0.33 per 1,000 pages, is the cheapest page-to-text path published by any vendor, language model or otherwise. GPT-6 Luna is next at about $0.62, or about $0.31 through the OpenAI Batch API, then GPT-5.4-nano at about $1.44. All three are below the $1.50 that AWS Textract, Azure Document Intelligence Read and Google Enterprise Document OCR charge for plain text extraction.

Is LLM OCR cheaper than traditional OCR?

It now can be, which was not true a year ago. The cheapest language model paths, Gemini 2.5 Flash-Lite at about $0.33, GPT-6 Luna at about $0.62 and GPT-5.4-nano at about $1.44 per 1,000 pages, sit below the $1.50 dedicated OCR rate. Mid-tier models do not: Claude Sonnet 5 at about $12.93 is roughly 8.6 times the dedicated rate. The answer depends entirely on which model you pick.

Why are published LLM OCR cost estimates so low?

Because almost all of them count input tokens only. Transcribing a page produces roughly 750 output tokens, billed at five to eight times the input rate, and that output is 60 to 65 percent of an OpenAI bill, 58 to 71 percent of a Claude bill and 92 to 96 percent of a Gemini bill. An input-only estimate understates the real cost by roughly two times on OpenAI and thirteen times on Gemini.

How do you convert token pricing to a per-page OCR rate?

Three numbers. First the image tokens for one page, which comes from each vendor patch formula: OpenAI uses 32 by 32 pixel patches, Anthropic uses 28 by 28. Second the output tokens for the transcription, roughly 750 for a full page. Third the per-million input and output prices. Multiply, add, and multiply by 1,000. State your page size and output length, because both change the answer.

Do the cheap models read the page at full resolution?

On Anthropic, no. Claude standard-tier models are capped at 1,568 visual tokens against 4,784 on the high-resolution tier, shrinking a letter page from 2,714 to about 1,560. On OpenAI, yes: the current vision guide gives GPT-5.4-mini and nano the same 2,500 patch budget and 1.2 multiplier as the full models, so a 150 DPI letter page is 2,496 tokens on every one of them.

Does batch processing reduce LLM OCR costs?

Yes, and it is the largest discount that actually applies. OpenAI, Google and Mistral all offer 50 percent off through a batch or asynchronous endpoint in exchange for a turnaround window of up to 24 hours. Document backlogs are the ideal workload for it. Prompt caching, which looks bigger, rarely helps, because the page image is the part of the request that changes every call.

Is an LLM accurate enough to replace OCR?

It depends on what you do with the output. A language model reads handwriting, odd layouts and poor scans better than a classical engine, but it returns no per-value confidence score and can produce a fluent, plausible, wrong value on a field it could not read. A dedicated engine tells you it read a digit at 62 percent confidence so you can route it for review. Most production pipelines use both.

What does LLM OCR cost for 1 million pages?

At the cheapest end, about $330 on Gemini 2.5 Flash-Lite or about $620 on GPT-6 Luna, falling to roughly $310 in batch mode, or $1,440 on GPT-5.4-nano. In the middle, about $5,250 on GPT-5.4-mini or $5,310 on Claude Haiku 4.5. At the top, about $32,320 on Claude Opus 5 and $62,460 on GPT-6 Astra. Dedicated OCR at that volume is $1,500 and falls to $600 per million on the tiered vendors.

Which LLM is best for document data extraction?

GPT-5.4-mini and Claude Haiku 4.5 are the usual working answers, both around $5 per 1,000 pages, cheap enough for volume and strong enough for structured fields. Move up to Claude Sonnet 5 or GPT-6 Sol when the document genuinely needs reasoning. Use Gemini 2.5 Flash-Lite or GPT-6 Luna when you are transcribing clean, predictable pages at scale.