Tools: Comparing AI Video Generator Prompt Formats: What Actually Matters in 2026

The Core Difference: Temporal vs Spatial ## Prompt Structure Patterns That Work ## For Sora ## For Midjourney Video ## For Veo ## What I Learned From Analyzing 500+ Prompts ## Extracting Prompts From Existing Videos ## Platform Selection Guide ## Practical Tips Anyone who has worked with multiple AI video generators knows the frustration: a prompt that produces stunning results on one platform falls completely flat on another. After spending months analyzing prompt patterns across platforms, here are the practical differences that actually matter. The most fundamental difference between AI video generators isn't resolution or duration — it's how they interpret temporal information. Sora excels at understanding motion descriptions. Prompts like "a camera slowly dollying forward through a misty forest at dawn" translate almost literally into camera movement. The model handles temporal progression naturally. Midjourney (in video mode) still leans heavily on its image generation roots. It interprets prompts spatially first, then infers motion. This means composition-heavy prompts tend to produce better results than motion-heavy ones. Veo sits somewhere in between, with particularly strong performance on prompts that describe physical interactions — things falling, splashing, or colliding. After extracting and comparing prompt patterns from existing videos, a few non-obvious patterns emerged: 1. Specificity has diminishing returns Adding more adjectives past a certain point actually degrades quality on all platforms. The sweet spot seems to be 3-4 descriptive elements per scene component. 2. Reference framing beats description Saying "shot like a Wes Anderson film" produces more coherent results than trying to describe symmetrical composition, pastel colors, and centered framing separately. 3. Negative prompts matter more for video Unlike image generation where negative prompts are optional refinements, video generation significantly benefits from specifying what to avoid — especially regarding temporal artifacts. One approach that has worked well is reverse-engineering prompts from existing video content. The process involves: Tools like TubePrompter automate this process, analyzing video frames and generating prompts tailored to specific AI generators. For Midjourney-specific workflows, the prompt templates section has some useful starting points. The gap between platforms is narrowing rapidly, but understanding their current strengths helps allocate effort to where it produces the best results. What prompt patterns have you found work best across different AI video generators? Share your experience in the comments. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: [Camera movement] of [subject performing action] in [environment], [lighting description], [mood/atmosphere], [style reference] Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: [Camera movement] of [subject performing action] in [environment], [lighting description], [mood/atmosphere], [style reference] CODE_BLOCK: [Camera movement] of [subject performing action] in [environment], [lighting description], [mood/atmosphere], [style reference] CODE_BLOCK: [Composition description], [subject details], [color palette], [artistic style], --video --duration [seconds] Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: [Composition description], [subject details], [color palette], [artistic style], --video --duration [seconds] CODE_BLOCK: [Composition description], [subject details], [color palette], [artistic style], --video --duration [seconds] CODE_BLOCK: [Scene description with physical interactions], [environmental details], [realistic/cinematic style], [temporal progression hints] Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: [Scene description with physical interactions], [environmental details], [realistic/cinematic style], [temporal progression hints] CODE_BLOCK: [Scene description with physical interactions], [environmental details], [realistic/cinematic style], [temporal progression hints] - Frame extraction: Pulling keyframes at scene transitions - Scene decomposition: Breaking each frame into compositional elements - Motion analysis: Identifying camera and subject movement patterns - Prompt assembly: Combining elements into platform-specific format - Start with your strongest reference video and extract its visual DNA before writing prompts from scratch - Keep a prompt library organized by platform — cross-platform prompts rarely work well - Test prompt variations in batches — small wording changes can produce dramatically different results - Document what fails — negative knowledge is just as valuable as positive results