DeepSeek OCR vs Mistral OCR Pricing

Short answer: DeepSeek OCR is cheaper and Mistral OCR is easier. Self-hosting DeepSeek-OCR on a busy rented A100-40G works out around $0.24 per 1,000 pages, against $4.00 per 1,000 for Mistral OCR 4.1 or $2.00 with batch processing. That is roughly a 16x gap on compute, and it exists because Mistral runs the servers and DeepSeek does not. Neither model returns invoice fields. Both return markdown, and turning markdown into a posted bill is a separate build.

Both figures were read from primary sources on 21 August 2026: Mistral's API pricing page, and DeepSeek's own paper plus Lambda's published GPU rates. Here is how the two actually compare when the documents in question are US vendor invoices.

DeepSeek OCR vs Mistral OCR pricing, side by side

Mistral also publishes two rates people confuse. Plain OCR is $4 per 1,000 pages on mistral-ocr-latest. A separate Document AI tier is $5 per 1,000 pages, and OCR run through the Libraries tool is billed at $3 per 1,000. Batch processing halves the standard rate. Our full breakdown of Mistral OCR pricing works through all three meters, and the DeepSeek OCR pricing page shows the GPU arithmetic behind the $0.24 figure at every volume tier.

Where the 16x price gap actually comes from

It is tempting to read a $0.24 figure next to a $4.00 figure and conclude that Mistral is overcharging by a factor of sixteen. That is not what the numbers say.

DeepSeek's $0.24 is the marginal cost of a page on a GPU that never stops working. It assumes you rent an A100 40 GB at Lambda's published $1.99 an hour and keep it fed at the 8,333 pages an hour DeepSeek reports in its paper. Miss either condition and the number moves fast. A finance team running 1,000 invoices a month needs about seven minutes of that GPU and still pays for a full billable hour, so their real rate is $1.99 per 1,000 pages. At that volume Mistral's batch rate of $2.00 is a dead heat, and Mistral requires no server, no CUDA version pinning and no on-call rotation.

The crossover sits somewhere around 50,000 pages a month. Below it, the price difference is rounding error against engineering time. Above it, the gap turns into a real budget line: 500,000 pages a month costs $2,000 on Mistral's standard rate and roughly $120 of rented GPU on DeepSeek. If you already run GPU infrastructure and employ people who ship model servers, that difference pays for the build quickly.

The DeepSeek column is GPU rental only. It does not include the engineer who built the pipeline, and that omission is larger than every number in the table.

What each model actually gives you back

This is the part that decides most real projects, and it is the same answer for both: neither model returns invoice fields.

Feed a vendor bill to DeepSeek-OCR and you get markdown. Feed the same bill to Mistral OCR and you get markdown. What you do not get from either is an invoice number in one key, a vendor name in another, a due date parsed as a date, a tax total that reconciles, and a list of line items each carrying a quantity, a unit price and an extended amount. Every one of those has to be produced by something you write.

That something is not small. In production it means rasterizing PDFs, retrying failed pages, prompting or parsing the markdown into a schema, normalizing dates and currency symbols across vendors who all format them differently, checking that line items sum to the invoice total, matching against a purchase order, and routing whatever fails to a human queue. Teams consistently underestimate the last one. Exceptions are where the hours go.

So the honest framing is that DeepSeek and Mistral compete on the cheapest layer of the stack. The layer above them, the one that turns text into a posted transaction, costs the same either way and dwarfs both.

Accuracy: what the DeepSeek paper actually claims

DeepSeek is unusually direct about its own limits. The DeepSeek-OCR paper states that "when the number of text tokens is within 10 times that of vision tokens (i.e., a compression ratio < 10x), the model can achieve decoding (OCR) precision of 97%. Even at a compression ratio of 20x, the OCR accuracy still remains at about 60%."

Read that carefully before it goes in a business case. 97% character precision is a strong result for an open 3.3 billion parameter model, and it is not the same as 97% of invoices being correct. A typical vendor bill carries perhaps forty values a bookkeeper cares about. A total that is wrong in one digit is not 97% right, it is wrong, and it will be wrong in the general ledger. Mistral publishes no comparable precision figure at all, which makes a head-to-head accuracy claim impossible to make honestly.

What that means practically: whichever engine you pick, budget for a validation layer. Totals that reconcile to line items, dates that parse, vendors that match a master list, tax that recomputes. That layer catches the failure modes both models share, and it is the only thing that makes either of them safe for financial data.

On efficiency, DeepSeek's benchmark claims are genuinely impressive. On OmniDocBench it "surpasses GOT-OCR2.0 (256 tokens/page) using only 100 vision tokens, and outperforms MinerU2.0 (6000+ tokens per page on average) while utilizing fewer than 800 vision tokens." Fewer tokens for equal or better output is exactly what drives the low per-page cost.

The costs that never appear in a per-page comparison

Self-hosting is a real operational commitment, and it is worth listing what you take on rather than gesturing at it.

• Version pinning. The DeepSeek repositories document CUDA 11.8 with PyTorch 2.6.0 and install flash-attn 2.7.3. That stack has to keep working across driver updates.
• Uptime. A hosted API is somebody else's pager. A self-hosted model server is yours, which means something has to notice when it stops answering at three in the morning and wake a person who can restart it.
• Idle GPU. The cheapest per-page rate assumes a saturated card. Real invoice volume arrives in bursts around month end, so either you accept idle spend or you build autoscaling, which is more engineering.
• Rasterization. Somebody has to turn multi-page PDFs into page images reliably, including the ones that arrive as email attachments inside forwarded threads.
• Licensing review. DeepSeek-OCR is MIT. DeepSeek-OCR 2, released in January 2026, is Apache 2.0, which adds an express patent grant plus attribution and NOTICE obligations. If you redistribute the model inside a product you ship, that is a legal conversation rather than a version bump.

Against that, Mistral's operational cost is an API key and a retry loop. For most US finance teams, that difference is worth far more than $3.76 per 1,000 pages.

Which one should you pick

Pick DeepSeek-OCR if you are past roughly 50,000 pages a month, you already run GPU infrastructure with engineers attached to it, or your documents legally cannot leave your network. Open weights on your own hardware is a materially different compliance posture from any API, and for teams under contractual data residency requirements it can be the only acceptable answer. Note that hosting a Chinese-developed model may still raise questions in some US procurement processes, even though the weights are permissively licensed and run entirely offline.

Pick Mistral OCR if you want good markdown from documents this week without owning a server, or your volume sits below the crossover where the price difference stops being rounding error. At $2 per 1,000 pages on batch it is genuinely inexpensive for what it removes from your plate.

Pick neither if what you actually need is invoice data, not page text. Both of these are OCR engines, and an accounts payable workflow needs typed fields, line items, an approval trail and an export your accounting system will accept. If your goal is invoices in a spreadsheet or posted to your ledger on Monday, the per-page rate of the OCR layer is not the number you are optimizing, and building the other 90% to save $3.76 per 1,000 pages is a poor trade. That is the job our invoice data extraction API and bulk invoice upload do directly, with header fields and line items typed and validated on the way out.

If you want the wider market context, the same math against the managed invoice APIs is worth seeing: AWS Textract Analyze Expense and Azure AI Document Intelligence both land near $10 per 1,000 pages and both do return invoice fields, which is precisely what the cheap OCR engines leave you to build.

Last updated August 2026. Mistral rates read from mistral.ai on 21 August 2026. DeepSeek throughput and accuracy figures from the DeepSeek-OCR paper (arXiv 2510.18234) and the project repositories; GPU rates from Lambda's published on-demand list, read the same day. Model prices change without notice, so check the source before committing a budget.