Sora vs Runway vs Kling vs Veo: How AI Video Prompts Actually Differ

Ask Sora, Runway, Kling, and Veo to generate the same video from the same prompt, word for word, and you get four clips that move differently, not just look different. A prompt that produces a slow, cinematic push-in on Sora can produce almost no camera movement at all on Runway, because Runway rewards a short, front-loaded camera instruction that Sora’s flowing paragraph style buries in the middle of a sentence. Kling, given a prompt written for the other three, tends to read it as one continuous shot and skip the escalation a numbered shot list is built to signal. These four tools are not just different brands wrapping the same technology. Each one was trained to expect a different shape of prompt, and a prompt written for one is, at best, a rough draft for the other three.

Why the same prompt produces four different clips

The core difference is how much of the prompt each model treats as instruction versus atmosphere. Some tools parse a prompt almost like a shot list, where order and structure carry real weight: what comes first gets treated as the dominant instruction, and everything after is refinement. Others read the whole prompt as one continuous scene description and distribute their attention more evenly across it, which means a critical detail buried in the middle of a long paragraph can get treated with the same weight as an incidental one. None of this is documented cleanly by any of the four companies, so most of what prompt writers know about it comes from testing the same idea across all four and comparing what comes back.

Sora: flowing cinematic prose that reads like a shot description

Sora responds well to a prompt written the way a director might describe a shot to a cinematographer: full sentences, natural pacing, and physical detail woven into the description rather than tacked on as a separate instruction. A prompt naming the lighting, the camera’s single movement, and what changes across the clip, all in one flowing paragraph, tends to produce a more coherent result than the same information broken into fragments or bullet points. Sora also tends to reward specificity about time of day and material texture (weathered wood, wet asphalt, frost on glass) more than the other three, producing visibly better results when that detail sits in the prose rather than being assumed.

Runway: economy, and one camera instruction up front

Runway’s Gen-3 and Gen-4 models tend to do best with a shorter, more front-loaded prompt than Sora rewards. Leading with the camera move (a slow dolly in, a static locked shot, a handheld drift) before the subject and setting description tends to produce a more deliberate, controlled result than burying the camera instruction at the end of a long paragraph. Runway also responds more literally to word choice than the other three, so vague adjectives (nice lighting, dynamic movement) tend to produce a flatter result than a single specific choice (golden hour side light, a slow diagonal pan).

Kling: numbered shots and explicit physics

Kling handles multi-beat motion and physical interaction (fabric, water, collisions, hair) more reliably than the other three when a prompt names it explicitly, and it tends to reward a numbered or sequenced structure for anything longer than a single simple action, similar to how Seedance expects a shot list rather than one flowing description. A prompt that states the starting position, what physically happens, and the ending position tends to produce a clip with a real arc, while a prompt that only describes the opening frame tends to produce a clip that drifts without much happening at all.

Veo: grounded scene detail, and dialogue or sound baked into the prompt

Veo, particularly its newer versions, is the one of the four built to handle dialogue and ambient sound directly inside the prompt rather than as an afterthought, so a prompt that specifies a line of dialogue, its tone, and the ambient sound of the scene (traffic, wind, a coffee shop’s hum) tends to produce a noticeably more complete result than a prompt written as if sound weren’t part of the deliverable at all. Veo also tends to ground itself well in real-world physics and lighting when a prompt is specific about time of day and location type, closer to Sora’s strength in that respect than to Runway’s more literal, economy-driven style.

Translating one idea across all four

Take a single concept: a barista steaming milk behind a counter, morning light through a front window, the espresso machine hissing in the background. For Sora: "Morning light streams through a cafe’s front window as a barista steams milk behind the counter, steam curling upward and catching the light. The camera holds a static medium shot as the barista’s hands work the pitcher, the espresso machine hissing softly in the background. Warm color grade, natural film grain, 24fps." For Runway: "Static locked shot, barista steaming milk behind a cafe counter, morning light through the window, steam catching the light, espresso machine hissing." The camera instruction leads, and the rest follows in short, economical phrases. For Kling: "[2] shots, [4s], barista steams milk behind counter, morning light through window. Shot 1: pitcher tilts, milk swirls and steam rises. Shot 2: barista taps pitcher twice, pours into cup, foam settles." The numbered structure gives Kling an actual sequence of physical events to render. For Veo: "A barista steams milk behind a cafe counter in warm morning light, steam catching the light as it rises. The espresso machine hisses steadily in the background, and a customer off camera says, 'Just the usual, thanks,' in a relaxed, low tone. Static medium shot, natural ambient cafe noise." The dialogue and ambient sound are written directly into the scene rather than assumed.

Common mistakes

Writing one flowing paragraph for Runway and getting a passive, uncommitted camera move because the instruction was buried past the point the model weighs most heavily. Writing a short, fragment-style prompt for Sora and getting a flatter, less atmospheric clip than a fuller cinematic description would have produced. Describing only a starting frame for Kling and getting a clip that barely moves, because nothing told the model what physically happens across the shot. Leaving dialogue or sound out of a Veo prompt entirely and being surprised the clip feels muted or overly generic when that channel was available and simply unused.

None of these four tools is doing it wrong. Each one was trained around a different default expectation for how a prompt is structured, and a prompt genuinely needs to be reshaped, not just copy-pasted, to get a tool’s best result rather than its default one. Once a scene is described well for one of the four, adapting it for the other three is mechanical work: reordering what comes first, adding or removing structure, deciding whether sound belongs in the prompt at all.