I have to be honest: Qolaba is trying to improve, and that is a good thing.

They have recently added new models like Seedance 2.0 Omni and Kling 3.0 Pro, which is a step in the right direction. They also added Nano Banana 2 and Pro in 4K, Flux 2 Pro, GPT 2.0, Recraft 4, and Imagen 4, so credit where it is due.

That said, Qolaba’s 2026 pricing and credit deductions are still way too high compared to other platforms. I compare many AI platforms, including LTD platforms, and Qolaba continues to have the highest credit costs I have seen. When i say they are the highest, i mean it. I have looked at a lot of platforms.

For example, there are at least 12 other LTD platforms that deduct far fewer credits for the same models. The average for a plan that gives 6000 credits monthly on an ltd is around 480 for 720p full omni to 720 credits for the 1080p full Omni version of Seedance 2.0 10-second clip. For the multi-reference version of Seedance 2.0, the average i have seen on other platforms deduct around 170 credits for 720p to 345 credits for a 1080p 10-second clip.

On Qolaba, Seedance 2.0 deducts 1,023 credits for a 720p 10-second clip and 1,770 credits for a 1080p 10-second clip for the full omni version. On a 5,500-credit plan, that means users can only generate about five videos at 720p or three videos at 1080p. Other platforms allow roughly 2x to 4x more generations, depending on the resolution.

I have also seen another LTD platform offer unlimited 1080p lip-sync video generation for $179, with the ability to generate four videos in one minute. I will not name the company out of respect for Qolaba, but this shows how competitive the market has become. Because of this, I really think Qolaba needs to lower its credit deductions and make its pricing more reasonable if it wants to keep up.

Qolaba should also consider adding stronger video models, especially models that give users more control:

Kling 3.0 Motion Control
A separate Kling model that allows users to upload a video so the output can mimic the motion and lip-sync.

Kling Video 3.0 Omni
This would be more useful than standard Kling 3.0 or Kling 3.0 Pro because it supports multi-reference input. Kling Omni 3.0 allows users to upload up to five reference images and a video, and it also includes a 4K resolution option.

LTX 2.3
This would be a strong addition, especially with 4K support and lip-sync features.

Qolaba currently has regular Kling Pro, but that version does not offer the same multi-reference support as Kling Omni. Without custom audio upload and better reference controls, it feels more like random generation rather than something useful for controlled lip-sync or professional workflows.

For image models, Qolaba should consider adding:

Qwen-Image-2.0
A 7B-parameter unified model for text-to-image and image editing, known for native 2K resolution, strong text rendering inside images, and high realism.

Z Turbo
A very affordable image model that is especially good at realism.

Hi-Dream
The full image version, which is great for animation and fantasy-style images.

Qolaba does not need to add every model available. They just need to focus on adding the best and most useful models. For example, Kling Omni 3.0 would be more valuable than standard Kling or Kling Pro because it allows users to include images and video as references, giving them much more control.

I would also like to see Qolaba rethink its YouTube content. Right now, the content feels too much like advertising. They should create more practical tutorials, such as “How to create a poster” or “How to make a singing video with Qolaba tools.” However, to fully showcase those kinds of workflows, Qolaba needs to add the right models first.

Overall, Qolaba needs to study competing LTD platforms more seriously. If they did, they would quickly see two major issues: competitors update more frequently, and Qolaba is still overpriced. Some competing platforms offer the same models at credit costs that are 2x to 4x cheaper.

This is honest feedback. Qolaba is improving, but if they want to stay competitive, they need to lower credit deductions, add more useful controlled video models, and focus more on practical workflows that users actually need.