txtfetch vs Mindee

Mindee is subscription + credits for structured field extraction. Here's the honest comparison — pricing math, capabilities, and where each tool actually wins.

at a glance

Mindee

Billing
subscription + credits (1 credit = 1 page)
Rate used below
$0.05/pagedirectional
Free tier
Removed September 15, 2025 — 14-day trial only, no ongoing free tier.

txtfetch

Billing
per document, regardless of page count
Rate used below
flat monthly quota — see plans
Free tier
500 documents/month, free, ongoing (Hobby plan)

capability table

CapabilityMindeetxtfetch
Billing unitsubscription + creditsper document
Format coveragePDF, images — narrow, doc-type-specific models1,000+ formats (Apache Tika)
OCR for scans / images++
Table extraction~+*
Complex layout fidelity~~*
Structured field extraction+not yet*
Self-hosted option
Single HTTP endpoint++

Vendor-published benchmarks are marked directional throughout — see sources below. * txtfetch notes: Tables come back as structured rows/cells, not flattened text: ?format=markdown renders GFM pipe tables, ?format=json returns table elements with a cells array. Output is clean linear/structured text, not a visual layout reconstruction — by design, but worth knowing. Structured markdown and element-JSON document output ship today (?format=markdown / ?format=json). Schema-defined field extraction — pulling typed fields per your own schema — is on the roadmap, not shipped. We won't claim it early.

the cost calculator

Per page vs per document, worked out.

A 300-page PDF is 300 units of billing to Mindee. It's one document to txtfetch. Adjust the numbers to your own workload.

Mindeedirectional

Derived effective rate — not a published per-page price (see note) · $0.05/page

txtfetch

Mindee's figure is their cheapest OCR/base tier — forms, tables, and higher-fidelity models cost more on top. txtfetch bills a flat monthly document quota: the page count inside a document doesn't change your bill.

where mindee wins

  • Turnkey field extraction for common document types (invoices, receipts, passports, IDs) with minimal setup — you get named fields, not raw text.
  • Custom document APIs let non-ML teams train a field-extraction model on their own document types without building an ML pipeline.
  • Confidence scores per extracted field, useful for automating approval thresholds.

where txtfetch wins

  • Breadth: 1,000+ formats through Apache Tika, not a curated list of a dozen file types.
  • One HTTP endpoint — pass a URL or upload a file, get text back. No SDK, no async job polling required.
  • Priced per document, so a 300-page report doesn't cost 300x a one-pager.
  • A durable free tier (500 documents/month, ongoing) rather than a time-boxed trial.

What we don't claim yet: schema-defined structured field extraction or an uptime SLA. Those ship later — see the capability table above.

sources

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