This 'Aleph' From Runway Redefines Post-Production Video Editing With Just Text Prompts

The artificial intelligence landscape has evolved dramatically, and it's still commencing.

Since OpenAI’s launch of ChatGPT in 2022. That moment was more than a technical release—it sparked a cultural shift and ignited a global “LLM war.” Companies rushed to build their own large language models, racing to one-up each other with smarter, faster, more multimodal systems. Text generation quickly matured into visual synthesis, leading to the rise of powerful image models.

But video remained the ultimate frontier—a medium far richer in context, nuance, and realism.

Then came the heavy hitters. OpenAI teased Sora, a model capable of creating highly realistic videos from text prompts—cinematic, complex, and eerily lifelike.

And among others, notably include Google that made headlines with Veo 3, a third-generation video model that went viral for its astonishingly fluid footage and dynamic scenes. The realism, lighting fidelity, and movement captured imaginations across social media.

For its part, Runway has what it calls the Act-One and the Act-Two. But these tools, like Sora and others, mostly built content from scratch. That left a massive gap for filmmakers and editors working with real footage—people who didn’t want to create new scenes, but refine the ones they already had.

This is where Runway announced what might be the most practical and disruptive video model yet: 'Runway Aleph.'

Aleph marks a turning point in generative video.

It isn’t about conjuring entire scenes from the void, but rather editing existing footage with natural language. Imagine that someone shot a clip and want to remove someone from the background, add dramatic lighting, shift the camera angle, or even relight the scene as if it were dusk instead of noon—all using just a text prompt.

That is where Aleph can come useful.

Runway built Aleph using a proprietary “in-context” video architecture, which means that the model can understand the semantics of each frame, the structure of a scene, and the context of surrounding frames.

Users can tell it to change a woman’s dress from red to blue, replace cloudy skies with sunshine, remove bystanders, adjust time of day, apply new lens effects, or shift a static shot into a dolly zoom. It’s editing, stylizing, and cinematography—all merged into a single prompt-driven interface.

Critically, Aleph supports features like:

• Object removal and addition, which smartly fills in the background.
• Relighting and weather transformation, from sunny to rainy, or day to night.
• Style transfer using image prompts or reference footage.
• Scene expansion, letting you extend the scene beyond the original clip.
• Temporal coherence, ensuring changes apply consistently across all frames.

In all, its main use is to take real input footage and applies edits, transformations, or stylizations while preserving continuity, identity, and motion integrity.

It allows users to perform tasks like motion transfer (mapping one clip’s movement to another image or video), reference-based editing (where you guide Aleph using an image style), or even camera angle generation, effectively creating new shots of the same scene from different viewpoints.

In other words, he model excels at video-to-video transformation, distinguishing it from text-to-video models like Sora.

According to Runway in a dedicated help page, Aleph currently supports clips up to five seconds long, capped at 64MB in size, and works with aspect ratios like 16:9, 4:3, 1:1, and vertical formats like 9:16. Each second of video processed costs around 15 credits.

It’s available first to Enterprise and Creative Partners, with wider rollout promised later this year—including to free-tier users.

Industry publications have noted that Aleph could significantly reduce production complexity.

For instance, shooting a single master shot might be enough; reverse angles, cutaways, or mood adjustments can be generated after the fact. Indie creators and commercial editors could cut post-production time dramatically—tasks that previously required hours of keyframing, masking, or rotoscoping can now be performed with a single sentence.

Cinematographers, understandably, are both thrilled and anxious. Aleph's ability to simulate camera work—like moving from a wide shot to a close-up or creating artificial tracking shots—challenges traditional production roles. Some fear that high-end editing jobs might shrink, replaced by generative pipelines that small teams or even individuals can manage.

But others see Aleph as a powerful assistant, augmenting rather than replacing human creativity.

Compared to Sora or Veo 3, Aleph feels grounded. Where those tools pursue the dream of full synthetic filmmaking, Aleph brings the magic of generative AI into existing workflows—real tools for real filmmakers. It doesn’t replace the camera; it partners with it, bringing a layer of intelligence and adaptability that was unthinkable just a few years ago.