Gemini vs GPT OCR Pricing: Real Cost per 1,000 Invoice Pages

Last updated September 2026

Short answer: Gemini is cheaper, but by far less than the raw token prices suggest. Reading a US Letter invoice page and returning structured JSON costs about $0.32 per 1,000 pages on Gemini 2.5 Flash-Lite and about $0.41 per 1,000 pages on GPT-5 nano. Move up a tier and the gap stays narrow: Gemini 2.5 Flash lands near $1.86 and GPT-5 mini near $2.05. The interesting difference is not the headline rate at all. It is that Gemini bills a flat 258 tokens for a document page while GPT-5 bills 2,496 tokens for the identical image, roughly nine and a half times more, and yet the two bills end up within a few cents of each other because output tokens dominate both.

Every figure below is computed from the vendors' published rates and their published tokenizer rules, with the assumptions stated so you can re-run them against your own documents.

The assumptions behind every number here

Cost per page is meaningless without a model of the page. Here is mine, and it is deliberately ordinary: one US Letter invoice scanned at 150 DPI, which is 1,275 by 1,650 pixels. A 120 token instruction plus JSON schema. And 700 output tokens, which is what a typical supplier invoice with about ten line items produces once you ask for vendor, invoice number, dates, tax, totals and a line item array.

Image tokens then follow each vendor's own rule. Google documents a flat 258 tokens per document page, regardless of resolution. OpenAI's GPT-5 uses patch based tokenization: ceil(width/32) x ceil(height/32) patches, multiplied by 1.2 and rounded up, capped by a 2,500 patch budget. For our page that is 40 by 52 patches, so 2,080 patches, so 2,496 billable tokens. GPT-4o uses the older tile method: scale the shortest side to 768 pixels, count 512 pixel tiles, then 85 base tokens plus 170 per tile, which gives 765 tokens.

Which is cheaper for OCR, Gemini or GPT?

Gemini, at every comparable tier, but the margin is small at the cheap end and grows at the expensive end. Gemini 2.5 Flash-Lite processes 1,000 invoice pages for about $0.32 against $0.41 for GPT-5 nano, a difference of nine cents per thousand pages. At the top of each range the gap widens sharply, because GPT-5's larger image token count gets multiplied by a much larger input price.

Notice what happens between GPT-5 and Gemini 2.5 Pro. Their published token rates are identical on both sides, $1.25 per million in and $10.00 per million out. GPT-5 still costs 37 percent more per page, purely because it bills 2,496 image tokens where Gemini bills 258. Same rates, different bill, and the difference only becomes visible once you do the tokenizer arithmetic.

Why output tokens, not the image, decide your bill

The most common budgeting error in LLM document extraction is assuming the picture is the expensive part. It usually is not. On Gemini 2.5 Flash-Lite, output accounts for 88 percent of the cost per page. On GPT-5 nano it is 68 percent. Even on GPT-5, where the image genuinely is large, output is still 68 percent of the total.

The practical consequence: the cheapest thing you can do to either bill is shrink the JSON you ask for. Dropping fields you never use, returning ISO dates instead of prose, and not echoing the raw text back alongside the fields will cut more cost than any amount of image preprocessing. If your invoices average thirty line items instead of ten, your output tokens roughly triple and so does most of your bill.

Does scanning at higher DPI cost more?

Barely, on either platform, which surprises people who have used other vision APIs. Gemini charges a flat 258 tokens per document page whatever the resolution. GPT-5 resizes to fit 2,048 pixels and then applies a 2,500 patch budget, so a 300 DPI Letter page bills 3,000 tokens against 2,496 at 150 DPI, an increase of about 20 percent on the input portion only. Since input is a third of the GPT-5 bill, scanning everything at 300 DPI raises your total cost by roughly 7 percent.

That is worth knowing because the tradeoff runs the other way on accuracy. If a higher DPI scan turns an unreadable total into a readable one, it pays for itself many times over. Neither of these vendors gives you a financial reason to scan badly.

The Gemini promotional rate expires on 1 January 2027

Gemini 3.8 Flash, 3.7 Flash and 3.6 Flash are all priced at $0.75 input and $3.75 output per million tokens through 31 December 2026. On 1 January 2027 those rates double to $1.50 and $7.50. In per page terms that moves the cost from $2.91 to $5.82 per 1,000 invoice pages, overnight, with no change on your side.

If you are building a business case on the Gemini 3 Flash family right now, model the post promotional rate. A pilot that looks comfortable at $2.91 and a production budget signed at $5.82 are two different conversations, and the second one arrives on a date you already know.

The one Gemini billing rule nobody mentions

On Gemini 3 models, tokens from text that is natively embedded in a PDF are not charged at all. Only pages processed as images bill, under the image modality. That means a born digital vendor invoice, the kind your supplier generated from their accounting system, costs meaningfully less than a scan of the same invoice, and no published table can show your true effective rate because it depends on the mix of digital and scanned documents in your inbox.

For a typical US accounts payable inbox, where somewhere between half and three quarters of invoices arrive as born digital PDFs, this quietly makes Gemini cheaper than the table above suggests. OpenAI has no equivalent rule; a PDF page goes through the image tokenizer either way.

Is GPT-5 or Gemini better at reading invoices?

There is no independent benchmark that scores current GPT-5 and current Gemini head to head on US supplier invoices, and anyone who quotes you a precise accuracy percentage for that matchup is quoting something they cannot source. What does exist is a 2025 third party test by BusinesswareTech on scanned documents, which measured GPT-4o at 98.0 percent on header fields and 57.0 percent on line items, against Azure Document Intelligence at 93.0 and 87.0 and AWS Textract at 78.0 and 82.0.

That result is the shape of the problem, and it has held up across every informal test we have run since. General purpose language models are excellent at the fields printed once on the page, invoice number, date, vendor, total, and noticeably weaker at line item tables, especially tables that continue across a page break. A model that reads every word beautifully but drops the fourth row of a table is worse for accounts payable than one that reads slightly less cleanly and gets every row, because a missing row silently changes what you pay.

Do Gemini and OpenAI have a batch discount for OCR?

Both do, and both are a flat 50 percent off standard rates. That halves every figure in the table above: Gemini 2.5 Flash-Lite goes to $0.16 per 1,000 pages and GPT-5 nano to $0.21. If your invoices can wait until morning, and for accounts payable they almost always can, batch processing is the single largest cost lever available on either platform. It is also the one most teams skip because the synchronous API is easier to build against.

What neither price covers

Both of these numbers buy you a JSON response from a model. They do not buy validation that line items sum to the invoice total, vendor name normalization across the six ways one supplier writes its own name, duplicate detection, credit note handling, retries when the model returns malformed JSON, or an export path into your accounting system. That work is identical whichever model you pick, it is where the schedule actually goes, and it does not appear on any rate card.

It also does not scale down. A one person bookkeeping practice pays the same engineering cost as a shared services center to build that layer, which is why per page API pricing flatters small volumes so badly. At 2,000 invoices a month, $0.32 per 1,000 pages is roughly a dollar of API spend against several weeks of build. The arithmetic only favors building at volumes where the API bill is a real line in the budget.

Document type matters here too, more than model choice does. An invoice is a short, dense, highly structured page and it suits these token economics well. A forty page commercial lease is the opposite: mostly prose, mostly relevant, and expensive on exactly the output tokens that dominate the bill, which is why turning a lease into structured terms is priced and built as its own category rather than as an invoice pipeline with more pages.

Which should you actually pick?

Pick Gemini 2.5 Flash-Lite if cost dominates, your invoices are mostly clean and mostly born digital PDFs, and you have engineers to own the validation layer. At $0.32 per 1,000 pages, or $0.16 on batch, nothing else in this comparison is close on unit economics.

Pick GPT-5 mini if you want the strongest general reasoning per dollar for messy, unusual or multi language layouts and you can absorb roughly six times the unit cost. Its 2,496 image tokens per page buy real visual detail, and it handles unusual invoice structures more gracefully than the smallest Gemini models.

Pick neither if what you need is invoices arriving as validated rows you can post, rather than JSON you then have to police. Our own invoice data extraction API and the tool at the top of this page handle the totals checks, the line item splitting and the export to Excel, CSV or your accounting system, at a flat monthly rate with no token meter to model.

If you want the full rate card for either vendor rather than the two way comparison, we keep a maintained breakdown of Gemini OCR pricing and of OpenAI OCR pricing, both re-verified against the published rates. And if you are comparing these against an enterprise platform rather than another API, ABBYY Vantage pricing starts at $200 per 1,000 pages on a three year commitment, which puts the numbers on this page in useful perspective.