Advanced Guide • 12 min read
Multi-Modal Prompting: Images, Audio & Video AI Guide
\nMulti-modal prompting gives AI both text and media — images, audio, video, PDFs — as input. Instead of describing what you see, you show it. Gemini 2.0 leads for video analysis, GPT-4o for audio, and all three major models handle images well. Below are STCO templates for every modality and a model comparison chart.
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Definition: Multi-modal prompting gives AI both text and media — images, audio, video, PDFs — as input. Instead of describing what you see, you show it. Gemini 2.0 leads for video analysis, GPT-4o for audio, and all three major models handle images well. Below are STCO templates for every modality and a model c
Model Multi-Modal Capabilities
| Modality | GPT-4o | Gemini 2.0 | Claude 4 |
|---|---|---|---|
| Images | ✅ Strong | ✅ Strong | ✅ Strong |
| PDFs/Docs | ✅ | ✅ | ✅ Best |
| Audio | ✅ Native | ✅ Native | ❌ |
| Video | 🟡 Frames | ✅ Best | ❌ |
| Code Screenshots | ✅ | ✅ | ✅ Best |
STCO Templates by Modality
Image Analysis
System: Expert visual analyst with attention to detail. Task: Analyse this image and [extract text/identify objects/assess quality/describe scene]. Context: This image is from [source/purpose]. I need to [specific goal]. Output: Structured analysis: objects detected + text extracted + quality assessment + 3 actionable recommendations.
Video Summariser
System: Video content analyst who creates concise summaries. Task: Watch this video and create a timestamped summary. Context: This is a [meeting recording/tutorial/presentation]. Length: [X minutes]. I need to quickly understand the key points. Output: 1-paragraph summary + timestamped key moments (timestamp: key point) + action items + suggested follow-ups.
Document Extractor
System: Document processing specialist with OCR expertise. Task: Extract and structure all data from this uploaded document. Context: This is a [invoice/contract/report/form]. I need the data in a structured format for processing. Output: JSON-structured extraction of all fields + confidence score for each field + any unclear/ambiguous sections flagged.
📌 Key Takeaways
- Multi-modal prompting gives AI both text and media — images, audio, video, PDFs — as input.
- Instead of describing what you see, you show it.
- Gemini 2.0 leads for video analysis, GPT-4o for audio, and all three major models handle images well.
- The STCO framework (System, Task, Context, Output) provides the most effective structural approach.
- Use AI Prompt Architect to generate structured prompts instantly.
- ⚡Go Pro: Unlimited prompt generations, AI-powered Refine & Analyse, and priority support — from £9.99/mo
Frequently Asked Questions
What is multi-modal prompting?
Multi-modal prompting is giving AI both text AND other media — images, audio, video, PDFs — as input. Instead of describing an image in text, you upload the actual image and ask questions about it. GPT-4o, Gemini 2.0, and Claude 4 all support multi-modal input.
Which AI models support multi-modal input?
In 2026: GPT-4o (text + images + audio), Gemini 2.0 (text + images + video + audio — most capable), Claude 4 (text + images + PDFs). For video analysis, Gemini is the clear leader. For document/image analysis, all three perform well.
How do I write prompts for images?
Use STCO: System: "Expert image analyst." Task: "Describe what you see + analyse [specific aspect]." Context: "This image is from [context/purpose]." Output: "Structured analysis with: objects detected, text extracted, quality assessment, and actionable recommendations."
Build Multi-Modal Prompts
AI Prompt Architect generates STCO prompts for any modality.
Start Building →Multi-Modal Prompting: The Evidence
Every claim below is sourced from peer-reviewed research and industry reports.Browse all 141 citations →
Batch APIs drastically reduce high-volume costs.
OpenAI's Batch API offers 50% cost reduction ($7.50 vs $15.00/MTok on GPT-4o output) for jobs completed within a 24-hour window.
Without structured prompt pipelines with deterministic schemas, workloads cannot be batch-processed — every request requires real-time inference at full price.
OpenAI, 'Batch API' documentation, 2024JSON Schema enforcement eliminates parse errors.
OpenAI structured outputs with JSON Schema achieve 99.9% schema adherence vs <70% with unconstrained generation — a 30x reduction in parse failures.
Without schema enforcement, every 1M requests generate 300K+ malformed responses requiring retries, error handling, and downstream data corruption.
OpenAI, 'Structured Outputs: JSON Schema' documentation, 2024Retry logic with backoff yields 3x uptime.
Exponential backoff retry with jitter achieves 99.97% request success rate vs 99.9% without — reducing unhandled failures by 3.3x.
Without structured retry patterns, a single provider outage or rate-limit error propagates as a user-facing failure.
Amazon Web Services, 'Exponential Backoff and Jitter' reliability patterns, 2023Streaming structured data enables progressive rendering.
Streaming JSON objects with Zod validation reduces perceived latency from 3 seconds to 400ms (87% improvement) for AI-powered UI components.
Without streaming, users stare at blank spinners until the full response arrives, creating a sluggish experience that feels broken.
Vercel, 'AI SDK: Streaming Structured Data' documentation, 2024