DeepSeek OCR Pricing: What It Really Costs Per 1,000 Pages

Last updated July 2026.

DeepSeek OCR has no official API and therefore no official price. The model weights are free under an MIT license, so what you actually pay for is the GPU you run them on. Rent a mid-range GPU at about $1 an hour and the arithmetic lands somewhere between $0.20 and $2.00 per 1,000 pages depending entirely on how many pages an hour you can push through it. At low volume, self-hosting is dramatically more expensive than simply calling a hosted OCR API, and that is the part almost nobody tells you.

Is there a DeepSeek OCR API?

No. This is the single most important fact on this page, and it contradicts a lot of what is currently published.

DeepSeek's official API, documented at api-docs.deepseek.com, serves two models as of July 2026: deepseek-v4-flash and deepseek-v4-pro. Neither of them is DeepSeek-OCR. There is no endpoint you can point a document at and receive a per-page bill from DeepSeek. If you have read a comparison post quoting "DeepSeek OCR API pricing" against Azure or Google, that post is quoting either a third-party host's rate or nothing at all.

For completeness, here is what DeepSeek does charge on its official API, read from its own docs in July 2026. These are text model rates and you cannot run OCR through them.

DeepSeek-OCR lives somewhere else entirely: on Hugging Face, as open weights you download.

Is DeepSeek OCR free?

The weights are. The compute is not, and for most teams the compute is the entire cost.

DeepSeek-OCR is published under the MIT license as a roughly 6.7 GB safetensors checkpoint. You may download it, run it, modify it, fine-tune it on your own documents, and use it commercially without paying a license fee. That is genuinely permissive, and it is the reason the model matters.

What "free" hides is that a model is only useful while a GPU is running it, and GPUs bill by the hour whether or not a document is passing through. A license fee of zero and a cost of zero are very different things, and the gap between them is where self-hosting budgets die.

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

It costs your GPU hourly rate divided by your pages per hour, times 1,000. There is no other formula, because there is no vendor rate card to consult.

Two of those three numbers you can look up. GPU rates from RunPod's community cloud, read in July 2026, run about $0.99 an hour for an L40S, $1.39 for an A100 PCIe, $1.49 for an A100 SXM, and $2.89 to $3.19 for the H100 family. The third number, pages per hour, depends on your hardware, your resolution mode, your batch size, and how dense your pages are. Nobody can hand you that figure honestly. You have to measure it on your own documents.

So instead of inventing a benchmark, here is the arithmetic across a range of throughputs. Find the row that matches what you actually achieve.

The GPU rates above are real and dated. The throughput column is yours to fill in. We would rather give you a formula you can trust than a number we made up.

One structural advantage is worth knowing. DeepSeek-OCR compresses a page into very few vision tokens: 64 in Tiny mode, 100 in Small, 256 in Base, 400 in Large, plus a dynamic mode the authors call Gundam. Fewer tokens per page means more pages per hour on the same card, which is exactly the lever that moves the table above. Choosing Tiny over Large is a bigger cost decision than choosing your GPU.

How much does it cost to self-host DeepSeek OCR?

More than you think at low volume, and the reason is idle time.

Leave a single L40S running around the clock at $0.99 an hour and you will spend about $722 a month, roughly 730 hours, regardless of whether you process one page or one million. Divide that fixed bill by your actual monthly volume and the per-page picture inverts completely:

Read that table slowly, because it contains the whole argument. At 5,000 pages a month, the "free" model costs about $144 per 1,000 pages while Gemini 2.5 Flash-Lite reads the same pages for about $0.33 and Azure Read for about $1.50. You would be paying roughly 400 times the going rate for the privilege of owning the deployment.

Run the break-even and it is stark. That $722 monthly GPU bill only undercuts Azure Read, AWS Textract, and Google's Enterprise Document OCR, all of which sit near $1.50 per 1,000 pages, once you are pushing past roughly 480,000 pages a month. It only undercuts Gemini 2.5 Flash-Lite at about $0.33 per 1,000 pages once you pass roughly 2.2 million pages a month. Below those lines, a hosted API is cheaper, and it is cheaper before you have paid a single engineer.

The obvious response is to stop paying for idle time: spin the GPU up, run a batch, shut it down. That works, and it is what sensible teams do. It also means you now own a job scheduler, a cold-start problem, a queue, and a pager. Those are real costs that never appear on a pricing page.

Is DeepSeek OCR cheaper than Gemini, Azure, or Mistral?

Only at genuinely high, sustained volume, or when the documents cannot leave your network. Here is the honest comparison at July 2026 rates.

Three reasons still make self-hosting the right call, and none of them is the headline price. Data residency, when a contract or a regulator says documents may not touch a third-party API. Predictability, because a rented GPU costs the same on your worst day. And control, because you can fine-tune an open model on your own forms in a way no API will let you. We keep the wider vendor picture on our OCR API pricing comparison, the token arithmetic on the Gemini OCR pricing page, and the corrected rates on Mistral OCR pricing.

What about hosted DeepSeek OCR endpoints?

Several inference providers host the open weights so you can call them like an API. Rate aggregators list DeepInfra at roughly $0.03 per 1M input tokens and $0.10 per 1M output tokens, with Novita in the same neighborhood. We could not load DeepInfra's own model page to confirm those figures directly, so treat them as indicative rather than verified and check the provider's current page before you budget on them. We will not publish a vendor rate we have not read on the vendor's own site.

If you do go this route, note that you have quietly given up the two things that justified self-hosting in the first place: your documents are now leaving your network, and you are back on someone else's uptime.

What is DeepSeek OCR 2?

A newer checkpoint in the same family, published on Hugging Face as deepseek-ai/DeepSeek-OCR-2. It is the same deal as the original: open weights, no official API, no per-page price. Everything in this article applies to it unchanged, because the economics are set by the GPU underneath, not the checkpoint on top.

What none of these numbers include

Every figure on this page buys one thing: pixels turned into text. A document workflow needs considerably more, and this is where the money actually goes.

Most pipelines do not begin with a tidy folder of PDFs. They begin with an inbox, and something has to pull the attachments and their data out of incoming email before any OCR model sees a page. Then something has to classify what each document is, because a scanned stack is not sorted. Something has to turn raw text into named fields your systems recognize, and keep doing it when a vendor redesigns its invoice. Something has to check the numbers are sane. Something has to show a person the fields the model was unsure about, in a screen where they can fix them in seconds. Something has to write an audit trail. And something has to land clean records in your ERP without anyone retyping.

DeepSeek-OCR does none of that, and neither does Gemini, and neither does Azure Read. For a typical US mid-market team, that missing pipeline costs more in year one than every page of OCR they will ever run. It is worth pricing the whole job before a per-page figure decides your architecture.

The short version

DeepSeek OCR is free to license and not free to run. If you are processing millions of pages a month, or your documents legally cannot leave your infrastructure, the open weights are excellent and the per-page cost at high utilization is hard to beat. If you are processing tens of thousands of pages a month, a hosted API will be cheaper, faster to ship, and less to maintain, and the honest recommendation is to use one.

Work out your monthly page volume before anything else. That single number, not the model, decides this. If it sits below the break-even lines above, stop reading about GPUs.

If what you actually want is validated fields rather than raw text, DocuOCR ships the classification, extraction, validation, human review, audit trail, and export as a finished product, priced per page at about $14 to $20 per 1,000. Upload one of your own documents and see what the output looks like before you commit to building any of it.