DeepSeek OCR Alternatives for Production Invoice and Table Extraction

The short answer: if your documents are invoices, bank statements and remittance advices, the problem with DeepSeek-OCR 2 is not accuracy in general, it is tables specifically. On OmniDocBench v1.7 it scores 90.25 overall, which is respectable, but only 83.89 on Table TEDS. PaddleOCR-VL-1.6 scores 94.76 on the same column, free and under the same kind of permissive license. For a US accounts payable team, tables are not a detail of the job. They are the job.

This matters because of how people pick models. Almost everyone sorts the leaderboard by the Overall column, and the Overall column is an average of three unrelated things: text edit distance, table structure, and formula recognition. A model that reads running prose beautifully and reconstructs grids poorly gets a flattering composite score, and the flattery is invisible until you run a batch of invoices through it and start finding line items in the wrong columns.

The ranking inverts when you sort by the column that matters

Here is the finding that should change how you read that board. HunyuanOCR sits immediately below DeepSeek-OCR 2 on the composite, 18th against 17th, separated by three tenths of a point. Sort the same 32 systems by Table TEDS instead and HunyuanOCR jumps to 6th while DeepSeek-OCR 2 falls to 19th. Thirteen places, between two models a casual reading of the leaderboard would call equivalent. And HunyuanOCR does it at 1 billion parameters against DeepSeek's 3 billion.

Note the Gemini rows while you are here, because they contain a second inversion worth money. Gemini 3 Flash, the cheaper tier, beats Gemini 3 Pro on tables, 89.29 against 89.15. Not by much, but the direction is the surprise. If you were about to pay for the premium tier specifically to improve table extraction, the board says you would be paying more for slightly less.

The alternatives, and what each one actually costs you

PaddleOCR-VL-1.6, if you want the best table reader and can host it

It leads the board outright at 94.76 on tables, it is Apache-2.0 from the code down to the model weights, and it is 0.9 billion parameters, which is a third of DeepSeek's size. If your reason for looking at DeepSeek-OCR was open weights and low compute cost, this is a straight upgrade on the axis you care about. The compute is cheaper too, because it runs comfortably on a much smaller GPU. The catch is the one every US team hits and few notice: the general PP-OCRv5 recognizer scores 64.70 on English against 86.38 on Chinese, and you have to know to switch to the English-specific model. The PaddleOCR page works through the full picture, including what running it costs per 1,000 pages.

HunyuanOCR, if you want a table specialist in a small package

Sixth on tables at 1 billion parameters is a genuinely unusual combination, and it is the clearest example on the whole board of specialization beating scale. Its weakness is the opposite of DeepSeek's: a text edit distance of 0.088, noticeably worse than DeepSeek-OCR 2's 0.050. So it reconstructs grids well and reads dense prose less well. For a pure invoice line-item pipeline that trade is usually the right way round. For contracts and correspondence it is not.

MinerU2.5-Pro, strong on tables with a license you have to read

Second on tables at 93.42, and the licensing is where the diligence goes. Several of the strongest open-weight parsers carry revenue caps that a permissive-looking GitHub badge does not advertise: MinerU requires a commercial license above a monthly revenue threshold, and Marker's model weights are free only below a company revenue line. Neither of those is a problem for a small team and both are a problem for a company that grows past the threshold without anyone rechecking. DeepSeek's licensing, to its credit, has no such cap: v1 is MIT and v2 is Apache-2.0, flat.

Gemini 3 Flash, if you would rather not run a GPU at all

At 89.29 on tables it is 5.4 points ahead of DeepSeek-OCR 2 and it arrives as a managed endpoint with a published price, no checkpoint to host and no CUDA version to pin. That last part is worth more than teams expect. DeepSeek-OCR documents an environment of CUDA 11.8 with PyTorch 2.6.0, vLLM 0.8.5 and flash-attn 2.7.3, and neither repository has been pushed to since early February 2026, so the pinning is on you indefinitely.

Mistral OCR, which is the one switch that would make this worse

It comes up constantly as the DeepSeek alternative and on this particular axis it is a step backwards: 76.78 on tables against DeepSeek-OCR 2's 83.89, and 27th overall against 17th. It is a managed API with real operational advantages and a published per-page rate, and if what you want is to stop running GPUs it is a reasonable destination. Just do not switch to it expecting better tables, because the one board that ranks both says otherwise.

AWS Textract, Azure and Google, where you cannot check at all

The three most widely deployed document APIs in the United States publish no accuracy figure and appear on no public leaderboard, so there is no honest row to put them in the table above. What is published is the price of asking for a table, and it is not the price of asking for text. Textract charges $1.50 per 1,000 pages for plain text and $15.00 per 1,000 for the TABLES feature, a tenfold step. Azure Read is $1.50 and Layout, which is what actually returns tables, is $10.00. Whichever way you go, the moment your requirement includes a grid, the meter changes.

The license trap nobody flags until legal does

Three of the models above ship under terms that look identical on a repository badge and are not. Apache-2.0 adds an express patent grant and a NOTICE obligation that MIT does not carry, which is why DeepSeek's move from MIT in v1 to Apache-2.0 in v2 is a fresh review rather than a formality if your process has separate checklists per license family. Revenue-capped licenses are a different animal again, because compliance is a function of how well your company does, and nobody re-reads a model license after a good quarter. Whatever you pick, put the license in the same decision document as the benchmark score. They expire on different schedules.

What none of these models do

Every option on this page returns markdown, or text and coordinates, or a JSON tree of page elements. None of them returns a checked invoice. The distance between those two things is the part that takes the time: classifying the document before you parse it, mapping extracted cells onto your chart of accounts, attaching a confidence to each field rather than to the page, routing the values below threshold to a human instead of into your ledger, and exporting something your accounting system will accept without a spreadsheet in the middle. A benchmark score of 94 on tables still leaves six pages in every hundred that need a person, and the pipeline is what decides whether that person sees them.

That is also the honest case for and against self-hosting. If you are pushing several million pages a month and your team wants to own a GPU service, the compute really is dramatically cheaper and you should do it. Below that, the GPU idles and a managed API wins on arithmetic alone. The DeepSeek OCR page shows the break-even worked out in full, at roughly 1.34 million pages a month, and the OCR accuracy comparison has the whole board with the caveats about what its composite score does and does not mean. Once the line items are out and reconciled, the next job is usually categorizing that spend against your chart of accounts, which is a different piece of software entirely.

How to choose in about five minutes

Pull twenty of your genuinely worst documents, not your cleanest ones. If they are table-heavy, start with PaddleOCR-VL-1.6 and compare against HunyuanOCR, and treat DeepSeek-OCR 2 as the prose baseline rather than the target. If they are prose-heavy with occasional tables, DeepSeek-OCR 2 is a perfectly sensible choice and its 0.050 text edit distance is better than every general-purpose frontier model on the board. If you are below roughly a million pages a month, skip the GPU question and price a managed API first. And whichever you choose, measure it on your own documents, because every number on this page was produced on somebody else's.