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light estimateLast updated 2026-08-20

Microsoft Azure vs Z.ai

Azure Document Intelligence (Read) vs GLM-OCR for ocr & document extraction. Max pages: 2000 vs 100 — edge Microsoft Azure Computed from public benchmarks with dated sources; updated 2026-08-20.

Head to head
Azure Document Intelligence (Read) compared with GLM-OCR per decision axis
AxisAzure Document Intelligence (Read)GLM-OCREdge
Price$1.5 per 1000 pages—*—
Score—95.22—
Max pages2000100Microsoft Azure
tables✓✓—
handwriting✓✓—
JSON✓✓—

* token-/credit-priced — the headline understates real per-unit cost, so no price edge is awarded.

Strengths & caveats
Microsoft AzureBroadest language coverage for print/handwriting, Read OCR $1.50/1000 (->$0.60 above 1M), strong Layout API for tables/structure, mature prebuilt models (invoice, receipt, ID) and JSON output. No public OmniDocBench score (null); Layout/prebuilt $10/1000 and Custom Extraction $30/1000 plus $6/1000 add-ons (formula, high-res) push real cost up well beyond Read pricing.Z.aiHighest published independent OmniDocBench composite of any hosted API in this table (95.22 on v1.6; Z.ai cites 94.62 on v1.5), callable at api.z.ai/api/paas/v4/layout_parsing with text, markdown and image-link output, HTML table reconstruction and handwriting support; the underlying 0.9B model is also self-hostable, and throughput is quoted at 1.86 PDF pages/second. Priced per token ($0.03 per million tokens for both input and output), not per page, so no comparable $/1000-pages rate exists without estimating — excluded from the price axis. Tight caps (100 pages per document, PDF 50MB, single image 10MB) rule it out for large documents, the documented language list is narrower than the hyperscalers', and China-hosted inference may raise data-residency questions for EU/US buyers.
Sources