Gemini OCR vs Mistral OCR: Pricing, Accuracy and API Comparison for Invoices

Gemini OCR vs Mistral OCR: the short answer

Gemini OCR costs roughly $0.27 to $1.58 per 1,000 invoice pages depending on the model, and Mistral OCR 4 costs a flat $4 per 1,000 pages, or $2 through the Batch API. Gemini is cheaper on raw price and currently scores higher on independent community accuracy voting. Mistral returns bounding boxes, per block confidence scores and typed block classification, bills a number you can forecast, and can be self hosted as a single container. Neither returns a finished invoice spreadsheet.

The choice usually comes down to one question: do you want the lowest possible unit cost, or do you want spatial data and a predictable bill? If you are building a pipeline where an engineer will map fields anyway, Gemini is hard to beat on price. If you need to draw a box around where a total was found, or you cannot send documents to a hosted API at all, Mistral is the one that can actually do it.

Cost per 1,000 invoice pages

Google publishes dollars per million tokens and Mistral publishes dollars per 1,000 pages. The table converts both into the same unit. Gemini figures assume 258 input tokens per document page, which is the rate stated in Google document processing documentation, plus 600 output tokens for a structured invoice record. Change that output assumption and the Gemini column moves; the Mistral column does not move at all, which is the whole point of the difference.

Gemini rates are from the official Gemini API pricing page and Mistral rates from the official Mistral API pricing page, both checked in August 2026. The Gemini totals are arithmetic on those published rates, not figures Google publishes directly, because Google does not publish a per page number. Reasoning tokens are excluded from the table and bill at the output rate on top, which is the single largest reason a real Gemini bill comes in above an estimate.

One consequence worth stating plainly: at 100,000 invoice pages a year, Gemini 2.5 Flash-Lite is about $27 and Mistral OCR is about $400. Both are small enough that the API bill is not what decides this. The engineering around it is.

Accuracy: what independent testing shows

Both vendors publish benchmarks in which they win. Mistral has published that Mistral OCR scores 94.9 percent against 88.49 percent for Gemini 2.0 on its own internal dataset, and Reducto reported that its own testing did not reproduce those numbers. Treat any self published figure accordingly, including the ones in our own favor.

OCR Arena runs blind community voting between OCR models on real documents, which makes it the most useful third party signal currently available. Its published head to head record:

Two honest caveats. First, that comparison is against Mistral OCR v3, not the newer Mistral OCR 4 released in June 2026, so the gap on current models is probably narrower than the table implies. Second, community voting rewards clean, readable transcription. It does not specifically measure whether the fourth row of a continuation table survived, which is the thing that actually matters for accounts payable. A model can win on transcription and still lose you a line item.

That is why the four step test above matters more than any leaderboard. Elsewhere in this market Veryfi advertises 98.7 percent on invoices and receipts specifically, which is a reminder that a purpose built invoice extractor and a general OCR model are not competing on the same axis.

Capabilities beyond price and accuracy

Several teams pick Mistral despite the higher price, and it is usually one of these rows that decides it.

The self hosting row is the one that ends the conversation for regulated teams. Mistral OCR ships as a container you can run inside your own network, so invoices never leave it. Gemini is a hosted API with no on premise option, and if your controls do not allow vendor documents to reach a third party endpoint, price is irrelevant.

The bounding box row matters more than it looks. If a human ever has to approve an extraction, showing them a highlighted box on the original page is the difference between a two second check and a two minute one. Building that on top of Gemini means asking the model for coordinates and accepting that they are approximate.

Frequently asked questions

Is Mistral OCR better than Gemini?

On current independent testing, no. OCR Arena community voting puts Gemini 3 Flash at ELO 1821 and rank #1 against Mistral OCR v3 at 1576 and rank #9, with Gemini winning 20 of 23 direct matchups. Mistral is better on bounding boxes, confidence scores, self hosting and billing predictability. Mistral OCR 4 has not yet been measured head to head at that scale.

Which is cheaper, Gemini OCR or Mistral OCR?

Gemini, by a wide margin on the light models. One thousand invoice pages costs about $0.27 on Gemini 2.5 Flash-Lite and about $1.58 on Gemini 2.5 Flash, against a flat $4 per 1,000 pages for Mistral OCR 4 or $2 through the Batch API. The Gemini figure moves with prompt length and reasoning tokens; the Mistral figure does not move at all.

Does Gemini have OCR?

Not as a separate product. Gemini reads PDFs and images natively as part of the model, so there is no OCR endpoint and no OCR price. Each document page counts as 258 input tokens under the image modality. On Gemini 3 models you are not charged for tokens from text already embedded in a PDF, so a digital invoice costs less to read than a scan.

Is Mistral OCR free?

No. Mistral OCR 4 is $4 per 1,000 pages on the API and $2 per 1,000 pages through Batch, with Document AI at $5 per 1,000 pages for structured annotations. Mistral offers limited free experimentation tiers for testing, but production use is paid. Gemini has a genuine free tier, with the caveat that Google uses free tier content to improve its products.

Can Mistral OCR be self hosted?

Yes. Mistral OCR is available as a single self contained container you can deploy inside your own infrastructure, which keeps documents on your network. This is its clearest advantage over Gemini for regulated industries, healthcare, legal and government work. Gemini offers no on premise deployment.

Which Gemini model is best for invoice OCR?

Gemini 2.5 Flash-Lite for volume at roughly $0.27 per 1,000 pages, and Gemini 2.5 Flash at about $1.58 when layouts are messy enough that the cheaper model starts dropping fields. Gemini 2.5 Pro at about $6.32 per 1,000 pages is rarely worth it for invoices, since invoice pages are short and the extra reasoning mostly adds output cost.

Do either of them return invoice line items?

Not reliably, and not in a finished form. Mistral OCR returns markdown with table structure preserved, so rows are visible but unmapped. Gemini returns whatever your JSON schema asks for, which means you own the schema and every failure when a vendor changes layout. Splitting, validating against totals and exporting is your code in both cases.

What does it cost to build an invoice pipeline on either one?

The API bill is the small part. At 100,000 pages a year you are looking at roughly $27 on Gemini 2.5 Flash-Lite or $400 on Mistral OCR. Schema design, line item logic, totals validation, retry handling, an export path and ongoing maintenance as vendor layouts drift are typically several engineer weeks up front and continuing after that.

The option neither price list mentions

Both of these are reading layers. You are buying text, or text with coordinates, and then building an invoice product on top of it. That is a reasonable choice when you have engineers and volume to justify it.

If what you actually want is invoice data in a spreadsheet, the model layer is a detour. InvoiceExtractor runs the extraction, the field mapping, the line item splitting and the totals validation, and gives you xlsx, csv, json or QBO at the end. Pricing is flat from $49 a month rather than metered per page, which works out around $19.60 per 1,000 pages on Starter and $14.90 on Plus, with no token accounting and no schema to maintain.

That is more per page than either raw API. It is considerably less than either raw API plus the engineer who makes it useful. Which side of that line you sit on depends entirely on whether you were planning to write that code anyway.