Phase 3: Video Generation · 55 min · Python · OpenAI API · fal.ai API

The Video Generation Landscape in 2026

Sora for storytelling, Veo for cinematic quality, Kling for speed + 4K, Runway for filmmaker control, Pika for social — knowing the landscape is video generation engineering.

What you will learn

Compare Sora 2, Veo 3/3.1, Kling 3.0, Runway Gen-4, Pika 2.5, Luma Ray3 — strengths, costs, and use cases
Understand open-source video models: Wan 2.7, LTX-Video
Map the cost landscape: $0.08 (Pika) to $0.50 (Sora/Veo) per video
Choose the right video model based on use case: storytelling, cinematic, social, filmmaker control

Objective

Create motion evidence that can be reviewed frame by frame, not only as a successful API response. In the paired studio lab, generate the same prompt with at least 2 video models (e.g., Kling via fal.ai and Runway Gen-4). Compare generation time, cost, and output quality. Write a comparison report with recommendations for different use cases.

The Problem

A team needs to generate a 10-second product video. They default to Sora 2 because "it's the best." It costs $0.50 and takes 2 minutes. For a social media ad, Pika 2.5 at $0.08 and 30 seconds would have been sufficient — a 6x cost savings and 4x speed improvement. They don't know the landscape, so they overpay and wait longer than necessary.

Video model landscape knowledge is tested in every video generation interview because it demonstrates you understand the cost/quality/speed trade-offs across providers.

What you'll build
A comparison tool that catalogs all major video generation models (Sora, Veo, Kling, Runway, Pika, Luma) with their costs, speeds, strengths, and use cases. Generate a model selection guide.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

Create a free account — the opening phases of 29 of 35 courses are free, no credit card.