MIST-Extract 1.0: document extraction built for African documents

Two open models that turn receipts, mobile-money messages, bills and letters into clean JSON, in English and African languages, with a confidence score on every field.

A lot of the paperwork that runs African businesses never touches a PDF. It arrives as a mobile-money SMS, a photo of a market receipt in naira, a screenshot of a bank alert, or a tenancy notice in Pidgin or Hausa. Pulling the amount, the date and the name out of that by hand is slow, and general-purpose extraction models were trained mostly on Western invoices.

Today we’re releasing MIST-Extract 1.0, built for exactly these documents.

What it does

Give it a JSON template and a document. The document can be text, a photo or scan, or a screenshot. It fills in the template:

template = {"recipient": "verbatim-string", "amount": "number",
            "fee": "number", "reference": "verbatim-string", "date": "date"}

"Your MTN MoMo transfer of GHS 250.00 to KOFI MENSAH was successful.
 Fee GHS 2.50. Ref 88213374. 14/10/2025"

→ {"recipient": "KOFI MENSAH", "amount": 250, "fee": 2.5,
   "reference": "88213374", "date": "2025-10-14"}

Every field comes back with a calibrated confidence, so you can accept the fields it’s sure of and send the rest to a person.

Two sizes

• MIST-Extract-1.0, 4B parameters: the most accurate.
• MIST-Extract-Flash-1.0, 2B parameters: about 10 documents a second on a single RTX A6000.

Both are full fine-tunes of Qwen3.5, released under Apache-2.0, and use the same template format as NuExtract, so existing templates work unchanged.

How it does

We tested both models against NuExtract3 (4B), the untuned Qwen3.5-4B, LiquidAI’s LFM2 Extract models and GLiNER2.5, on the same 2,016 held-out documents in 32 languages. Every model got the same input and the same scoring rules.

Field accuracy	MIST 4B	Flash 2B	NuExtract3	Qwen3.5-4B
All held-out documents	0.898	0.879	0.801	0.762
Receipt photos	1.000	0.999	0.925	0.738
African documents	0.931	0.912	0.919	0.890
Named entities, 21 African languages	0.728	0.701	0.393	0.432

The fields that matter most in these documents are dates, amounts and currencies:

Field type	MIST 4B	Flash 2B	NuExtract3
Dates	0.979	0.975	0.884
Amounts	0.984	0.970	0.964
Currency	0.975	0.975	0.966

On English documents of these kinds, MIST-Extract gets 91.8% of documents completely right, against 67.9% for NuExtract3. The gap is widest in languages that rarely appear in training data: in chiShona, Luo and isiXhosa, MIST-Extract-1.0 is 40 to 55 points ahead.

Confidence you can act on

A score is only useful if it means something. When MIST-Extract-1.0 says 0.66, it is right about 66% of the time; across held-out fields its calibration error is 0.008. On one receipt photo it misread the shop name, and told us so:

merchant: "GL HANDICRAFT & TAIL CHINGS"   0.50
date: "2018-03-27"                        0.95
total: 130                                0.94

Set a threshold, say 0.95: fields above it go straight through, and fields below it go to review.

Use it

vllm serve olaverse/MIST-Extract-1.0 \
  --limit-mm-per-prompt '{"image": 1, "video": 0}' \
  --chat-template-content-format openai \
  --max-model-len 131072

Then call it with any OpenAI client, passing your template as chat_template_kwargs. The model cards have full examples for text, images, instructions and per-field confidence.

MIST-Extract is built for African and English everyday documents. For general Western invoices and multi-page PDFs, a general-purpose extractor such as NuExtract3 is the better fit.

Get the models

• MIST-Extract-1.0 (4B)
• MIST-Extract-Flash-1.0 (2B)
• The MIST-Extract 1.0 collection

Building with these documents, or want to use MIST-Extract in production? Get in touch at olaverselab.com.