I Bought Every OpenArt Plan So You Don't Have To
Provides AI filmmakers with a detailed breakdown of credit economics across OpenArt tiers and model choices, helping them optimize generation costs versus output quality.
What this lesson covers
The creator breaks down OpenArt's subscription tiers and credit allocations by testing image and video models like Nano Banana Pro and Kling across different resolution settings. The video demonstrates how credit costs translate into actual output volume while showcasing features like parallel generations, OpenArt Arena, and MCP integration.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 00:47 ↗
OpenArt subscription tiers range from Starter ($13/month for 4,000 baseline credits) to Wonder ($175/month for 106,000 credits), with annual billing discounts up to 27%.
- 01:53 ↗
OpenArt Arena provides a public user-ranked leaderboard comparing image and video model performance categorized by specific use cases like ads, film, and animation.
- 02:50 ↗
Generating a 1K image with Nano Banana Pro costs 40 credits, yielding 100 images on the Starter plan rather than the theoretical maximum shown on pricing pages.
- 03:35 ↗
Upscaling Nano Banana Pro from 1K to 2K costs the same 40 credits while delivering significantly more realistic lighting and fine details like dust.
- 03:57 ↗
Starter plan users receive eight parallel generations, allowing multiple image creation tasks to process simultaneously in the background.
- 05:08 ↗
Generating video via Kling 3.0 at 720p costs 175 credits compared to 200 credits for Kling 2.6 at 1080p, though lower resolution leads to softer particle details.
Workflow outlined in the video
- Check OpenArt Arena rankings by specific use case (film, ads, animation) before selecting a model for production tasks.
- Use 2K image resolution instead of 1K when generating with Nano Banana Pro to achieve better detail and lighting without increasing credit costs.
- Leverage parallel generations to launch up to eight rendering tasks at once to speed up asset creation.
- Utilize the OpenArt MCP connector to prompt image and video generations directly inside LLM interfaces like Claude or ChatGPT.
- Factor in actual per-generation credit costs (e.g., 40 credits per image, 175-200 credits per video) when calculating tier needs rather than relying on marketing totals.
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.
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How to interpret the numbers
12,364 views captured 21 September 2026. ReelStack recommendation score: 31/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 45% performance: 0.19× views vs 3 other channel videos observed at a similar age. View-evidence factor 93% (views / (views + 1,000))
- 25% freshness: 88/100 with a 14-day half-life
- 20% momentum: 1355 views/day vs 15570 channel baseline (3 comparable uploads), with the same view-evidence factor
- 10% engagement: 7/100; likes + 4× comments, smoothed with a 500-view neutral prior