Notes by ReelStack · AI-assistedUpdated 27 September 2026

I Tried Every AI Video Generator - Seedance 2.5 vs Minimax h3 vs Flux 3

Navigating the rapidly expanding landscape of AI video generators requires structured benchmark testing across camera control, prompt adherence, and cost efficiency.

Video thumbnail: I Tried Every AI Video Generator - Seedance 2.5 vs Minimax h3 vs Flux 3
Original YouTube video

Dan Kieft

Published

Watch the original video ↗

What this lesson covers

Dan Kieft evaluates Seedance 2.5 against MiniMax H3, Flux 3, and Seedance 2.0 to help filmmakers choose the right AI video generator for specific tasks. The comparison breaks down technical specifications, credit costs, local running options, and benchmark tests across camera motion and cinematic prompts.

Key takeaways from the creator

AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.

  1. 01:00 ↗

    Seedance 2.0 supports generations up to 15 seconds in native 4K resolution at 6,000 OpenArt credits per clip.

  2. 01:34 ↗

    Seedance 2.5 provides up to 30-second generations at 720p resolution and accepts up to 50 reference assets including image, video, and audio inputs.

  3. 02:18 ↗

    MiniMax H3 generates up to 15-second clips at 2K resolution and offers a local execution option suitable for unrestricted workflow needs.

  4. 03:18 ↗

    Flux 3 outputs 1080p video up to 20 seconds long but incurs higher generation credit costs compared to competing models.

  5. 04:51 ↗

    Seedance 2.5 demonstrates strong prompt adherence for complex multi-stage camera motions such as crash zooms, whip pans, and earth zoom outs.

Workflow outlined in the video

  1. Select the appropriate AI video model based on project needs, using Seedance 2.0 for 4K quality, Seedance 2.5 for 30-second extended takes, or MiniMax H3 for local uncensored workflows.
  2. Structure complex camera prompts with clear sequence order, detailing initial moves, mid-shot transitions, and final framing.
  3. Compare model credit requirements across platforms like OpenArt to optimize generation budgets during production.
  4. Test prompting strategies across multiple models to determine which engine best matches specific stylistic requirements.

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

148,875 views captured 27 September 2026. ReelStack recommendation score: 41/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.

Show the calculation
  • 65% performance: 1.27× views vs 5 other channel videos observed at a similar age. View-evidence factor 99% (views / (views + 1,000))
  • 25% freshness: 16/100 with a 14-day half-life
  • Momentum pending: collecting daily snapshots; its 20% weight goes to observed performance, not free points
  • 10% engagement: 6/100; likes + 4× comments, smoothed with a 500-view neutral prior

Read our methodology and limitations →

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