Lesson 1091 of 2116
Multimodal AI Trade-offs: Vision, Audio, Video
Multimodal AI handles images, audio, and video. The performance varies by modality and the cost varies dramatically.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2AI model families: the multimodal capability map
- 3The premise
- 4AI and multimodal model input shapes
Concept cluster
Terms to connect while reading
Section 1
The premise
Multimodal capability and cost vary dramatically by modality; deliberate selection matters.
What AI does well here
- Test capability per modality on representative inputs
- Track cost per modality (video especially expensive)
- Choose modality-specific tools when general models underperform
- Pre-process inputs to consistent quality
What AI cannot do
- Get equal performance across all modalities from one model
- Eliminate the cost difference between text and video
- Predict modality capabilities in 18 months
Key terms in this lesson
Section 2
AI model families: the multimodal capability map
Section 3
The premise
No single family currently leads in every modality. Pick the model whose strongest modality matches your bottleneck task, even if it means using two providers.
What AI does well here
- Read images and answer questions about them when supported
- Transcribe audio when given a speech-capable model
- Generate images when given a generation-capable model
What AI cannot do
- Add modality support that the family doesn't have
- Match the leader in a modality outside its strength
- Handle multimodal output (images + text) coherently in many models
Section 4
AI and multimodal model input shapes
Section 5
The premise
A multimodal model is several models in one wrapper. Use the right input type for the task — sometimes pre-processing beats raw upload.
What AI does well here
- Compare costs of image vs OCR-then-text.
- Suggest when to convert PDFs to text first.
- Identify image-resolution effects.
What AI cannot do
- Replace specialized OCR for forms.
- Guarantee accuracy on charts.
- Avoid token spikes on large images.
Section 6
Working With Multimodal Models: Image and PDF Inputs
Section 7
The premise
Multimodal models read layouts and screenshots well. They are weaker at fine pixel measurement and at reading dense small text.
What AI does well here
- Describe screenshots, charts, and document layouts.
- Extract structured data from clear forms and tables.
What AI cannot do
- Reliably read tiny or low-contrast text.
- Make precise pixel-level measurements.
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