Google Veo 3.1 Deep Dive - Does it Beat Sora 2?
As advanced AI video generators introduce multi-image conditioning and precise start-end framing, filmmakers gain tighter control over complex narrative shots and multi-character consistency.
What this lesson covers
This tutorial explores the new Google Veo 3.1 video generator, highlighting its multi-image ingredients feature and start-and-end frame capabilities. The host tests these updates against other leading models, discussing workflow efficiency, generation costs, and visual consistency.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 00:17 ↗
Google Veo 3.1 introduces an ingredients feature allowing users to input up to three reference images to guide the generation.
- 00:48 ↗
Users can access Veo 3.1 via platforms like OpenArt, alongside other models such as Kling and Sora.
- 01:40 ↗
The generation settings for Veo 3.1 support 1080p resolution and integrated audio generation.
- 04:47 ↗
Start and end frame controls on Veo 3.1 enable smooth dolly transitions and visual progression between two distinct keyframes.
- 05:58 ↗
The video mode can be toggled between fast and normal speeds, affecting credit consumption and overall output quality.
Workflow outlined in the video
- Select an AI generation platform that supports Google Veo 3.1.
- Navigate to the element or ingredients section to upload up to three reference images (such as characters, styles, or backgrounds).
- Craft a detailed prompt describing the action, character identity lock, and scene dynamics.
- Configure output settings including 1080p resolution, audio options, and generation speed mode.
- Test start and end frame inputs to achieve controlled camera movements like forward dolly shots.
Before you use this workflow
These notes describe the source video at its publication date. Model access, pricing, connectors and interfaces may have changed. Check the original source and the provider’s current documentation before installing an add-on, connecting an account or spending credits.
ReelStack has not independently tested this workflow. Preview one representative shot and check motion, continuity and output quality before applying it to a full production. No result, cost saving or model capability is guaranteed.
How to interpret the numbers
37,320 views captured 23 October 2026. ReelStack recommendation score: 20/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 45% performance: 0.45× views vs 10 other channel videos observed at a similar age. View-evidence factor 97% (views / (views + 1,000))
- 25% freshness: 0/100 with a 14-day half-life
- 20% momentum: 9 views/day vs 18 channel baseline (4 comparable uploads), with the same view-evidence factor
- 10% engagement: 3/100; likes + 4× comments, smoothed with a 500-view neutral prior