As regards text-to-video functionality, the Dreamlux AI video platform does provide basic capabilities but with severe limitations. According to the NLP-Video Benchmark test in 2025, its text parsing engine can convert inputs of up to 500 words to 15-second video clips (at a resolution of 1080p and 30fps). Yet the recovery rate of complex semantics (e.g., metaphors and multi-role interactions) is only 68% (industry benchmarks such as Synthesia score 92%). For example, when the input “Beach runner interacting with Seagulls at sunset” is provided, the failure rate of synchronization of the character’s movement and the bird’s flying trajectory in the produced video is 37%, and the light and shade rendering error ΔE*94 is 6.3 (within 2.1 under control for paid software).
At the level of technical realization, Dreamlux AI video employs a text understanding model of the GPT-4 architecture. The single processing maximum is 300 words (2000 words are enabled by paid tools such as Pictory), and the average time taken to generate a 1-minute video is 7 minutes and 12 seconds (NVIDIA RTX 4090). The paid tools are reduced to 2 minutes and 38 seconds through distributed computing. The hardware requirements are extremely high: With 10-minute long text videos, the video memory usage reaches 14.8GB (beyond the upper range of consumer-grade graphics cards), forcing the resolution to be dropped down to 720p, resulting in a 23% loss in readability of the text overlay subtitles (paid software automatically adjusts the resolution for optimal clarity).
Functionally deficient, its multi-language support is limited – covering only 12 languages such as English and Chinese (54 languages supported by commercial tools), and the text-visual mapping error rate for small languages (such as Arabic) is as high as 29%. The MIT example in 2024 shows that when the Spanish prompt “festival de colores” (color festival) is input by users, the color saturation drift in the output video is ±34% (±9% for commercial software), and manual adjustment takes 4.5 minutes per scene. Moreover, there is a significant copyright risk: 7% of the training data content is unauthorized content (uncovered by Getty Images in a 2025 lawsuit), and the music/image infringement rate of user-generated videos is 0.9 times per thousand generations (0.1 times for paid tools).
The cost-efficiency ratio comparison indicates that even when the Dreamlux AI video is free, the total implicit cost of creating a one-hour text video (hardware loss 0.42+ electricity cost 0.18+ legal risk 0.65) equals 1.25 per hour, which is far more than that of Mid-tier paid software (e.g., InVideo, 0.37 per hour). In a typical situation, the marketing team @AdGenius used this tool to create a product explainer video. Because AI read “waterproof performance” instead of “dustproof performance,” the error percentage dropped to 0.3% after switching to Lumen5, which provided 158,500.
Alternative solution metrics show that paid tools lead on key indicators: Descript text-lip-sync error is only 0.08 seconds (Dreamlux 0.35 seconds), and it also supports real-time editing and multi-track synthesis. For users with limited budgets, Dreamlux AI video can be used as a prototype testing tool, while commercial-grade text-video creation relies on professional platforms for quality and compliance reasons.