Concept card

Multi-modal Model Landscape 2026 — Vision, Audio, Document

As of 2026-07-24T14:00:00Z

Edited by Cédric-Olivier Mâry, sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

What is Multi-modal Model Landscape 2026 — Vision, Audio, Document?

Multimodal document extraction moves SAP's straight-through-processing rate from 60-70% to 90-95%, because frontier models hit 95-98% accuracy on mixed-layout invoices versus 70-85% for traditional OCR plus rules.

Multi-modal models are large language models trained to read more than plain text — they ingest and reason over images and video, audio such as speech and environmental sound, and documents that mix text, tables and images in irregular layouts. For anyone building an SAP analytics or Joule-based agent, this category matters because most of the real input an enterprise generates never arrives as clean text. Invoices, purchase orders, freight bills, scanned contracts, audit evidence, dashboard screenshots, warehouse camera feeds and recorded customer calls are the actual substrate an agent must process before any conventional SAP data model can act on it. Of the three modalities, documents are the most consequential for SAP practitioners, because accounts-payable, supply-chain-receiving and audit-automation programmes have all been bottlenecked for years by the 15-30% error rate of classical OCR-plus-rules pipelines on mixed-layout PDFs.

How it works

Why it matters

Classical OCR + rule-engines mishandle invoices, freight bills and regulatory filings 15-30% of the time — the actual bottleneck behind SAP customers' accounts-payable and audit-automation programmes.
Each vendor leads a distinct modality: Claude on document/chart reading (SAP's Sapphire 2026 supply-chain agents for freight bills and customs forms), GPT-o4 on screenshot/UI reasoning (Microsoft Copilot Studio), Gemini 2.x on long-video and native audio (Databricks Mosaic industrial-IoT).
Joule Studio 2.0 (June 2026) ships native document-extraction primitives wrapping Claude, removing the need for a separate Tesseract + ABBYY + custom-regex pipeline.

Key points

Multi-modal models accept vision (images, charts, screenshots), audio (speech, environmental), and document (PDF, scanned forms) inputs — not just text; reasoning crosses modalities in a single inference.
Document modality is the SAP-consequential one — invoices, freight bills, audit PDFs are mixed-layout files classical OCR + rules mishandle 15-30% of the time; that error rate is the AP/supply-chain automation bottleneck.
Three vendor families 2026 — Claude (Anthropic, document + chart, SAP supply-chain agents, Joule Studio 2.0) · GPT-o4 vision (OpenAI, screenshot + UI navigation, Copilot Studio) · Gemini 2.x (Google, long-video up to 1h + native audio, Databricks Mosaic IoT).
Joule Studio 2.0 (June 2026) ships native document-extraction primitives wrapping Claude — no separate Tesseract + ABBYY + regex pipeline needed; the multi-modal model IS the pipeline.
Accuracy lift — 95-98% extraction on mixed-layout invoices vs 70-85% classical OCR + rules; STP from 60-70% to 90-95%; collapses AP human-review queue — the unit-economics bottleneck since 2018.
Routing taxonomy — document PDFs → Claude; UI screenshot navigation → GPT-o4; long video + call recording → Gemini 2.x; misrouting wastes prompt tokens and drops accuracy.
Context window limits matter — Claude handles PDFs up to 100 pages natively; beyond that, document chunking + retrieval (RAG over pages) is required; GPT-o4 vision handles up to 2048×2048 px images; Gemini 2.x handles up to 1h video.
SAP-specific use cases: AP invoice extraction (Claude, Joule Studio 2.0) · customs form processing (Claude, SAP TM) · desktop RPA screenshot navigation (GPT-o4, Copilot Studio) · factory camera QA inspection (Gemini, Vertex AI + Databricks).
Governance requirement — every document extraction pipeline must log the model version, confidence score per field, and human-override rate; Joule Studio 2.0 provides this natively; custom pipelines must build it.
When not to use — clean structured tabular data (use RPT, C237); plain unstructured text (use a text-LLM); real-time video streams with <500ms latency requirement (frontier models add 2-10s latency).

Terms used on this page

Multi-modal model: A foundation model that accepts inputs across multiple modalities (vision, audio, document, text) and reasons across them in a single inference call; distinct from a pipeline of single-modality models chained together.
Document modality: The multi-modal capability handling mixed-layout PDFs that combine text + tables + images — invoices, freight bills, audit evidence; the modality most consequential for SAP AP and supply-chain automation.
Straight-through-processing (STP): The share of inbound documents an automation programme processes end-to-end without human intervention; multi-modal models lift STP from 60-70% (classical OCR + rules) to 90-95% on mixed-layout invoices.
Joule Studio 2.0 document primitives: Native extraction primitives shipped in Joule Studio 2.0 (June 2026) that wrap Claude for the document path; remove the need for a separate OCR + rules pipeline in SAP AP and supply-chain-receiving agents.
Agentic-browser workload: An AI agent task that requires reading and navigating rendered UI screens (screenshots) rather than structured data or documents; GPT-o4 vision is the leading model for this class of workload in 2026.
Confidence score per field: The model's self-assessed probability that a given extracted field value is correct; required for governance — fields below a confidence threshold route to human review rather than automated processing.
Long-context document chunking: Splitting a document longer than the model's native context window (e.g. >100 pages for Claude) into overlapping chunks with retrieval-augmented reassembly; required for multi-hundred-page audit reports and regulatory filings.
Vision token cost: Multi-modal models charge additional tokens for image/PDF inputs beyond the text tokens; Claude charges ~1600 tokens per PDF page; GPT-o4 charges ~170 tokens per 512×512 tile; relevant for cost modelling at scale.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks · the facts worth quoting.

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