How well do different AI video models follow the same prompt? This AI video prompt adherence comparison tests six models through GLBGPT: Wan 3.0, Kling 3.0, Seedance 2.5, Grok Imagine, Happy Horse 1.0, and Veo 3.1. Each model receives the same prompt so you can see which one preserves the requested objects, camera moves, event order, and audio requirements.
Every main card uses one fixed English prompt and at least four model outputs. The cinematic card uses all six models; the other cards use four models selected from the same six-model set. We used 16:9 framing and 720p output, then inspected the returned videos and contact sheets for visible prompt coverage.
Быстрый ответ: There is no universal winner across all five prompts. Wan 3.0 produced the clearest camera coverage in the motion tests, Клинг 3.0 kept the pottery pattern most literally, and Seedance 2,5 kept the cinematic walk, turn, and tram arrival in the clearest order. Grok Imagine was strong on the market camera path, Happy Horse 1.0 kept the character and story beats readable, and Veo 3.1 returned usable files for the cinematic, motion, and pottery prompts, with shorter returned durations on the latter two.
What Is AI Video Prompt Adherence?
Prompt adherence is the degree to which a generated video preserves the details in your request. We checked named objects, colors and patterns, camera movement, event order, character continuity, and audio or dialogue constraints.
A clip can look polished and still miss the prompt. It may show convincing rain but lose the tram, turn a thin white wave into a different pattern, change the actor’s face, or stop before the final story beat. Each card therefore shows the exact prompt focus beside the model outputs and the observed misses.
The Six Models in This Comparison
All six models use the same access label in this article: GLBGPT. The table is the complete lineup; there is no separate model section that introduces only two providers.
| Модель | Доступ | Prompt coverage in this comparison | What the cards test |
|---|---|---|---|
| Wan 3.0 | GLBGPT | All five prompts | Camera movement, characters, motion, audio, story order |
| Клинг 3.0 | GLBGPT | All five prompts | Camera movement, characters, motion, audio, story order |
| Seedance 2,5 | GLBGPT | Cinematic, character, dialogue | Event order, character detail, dialogue scene |
| Grok Imagine | GLBGPT | Cinematic, motion, dialogue | Camera path, action coverage, two-speaker scene |
| Happy Horse 1.0 | GLBGPT | Cinematic, character, pottery | Character continuity and three-beat story coverage |
| Veo 3.1 | GLBGPT | Cinematic request, motion, pottery | Route acceptance, camera movement, story beats |
How We Tested the Six Models
We froze five prompts before comparing the outputs. The model changed from card to card, while the prompt, aspect ratio, resolution, duration, and audio setting stayed the same within each card. A representative GLBGPT request shape was:
{
"surface": "GLBGPT",
"model": "seedance-2.5",
"prompt": "the identical test prompt",
"aspect_ratio": "16:9",
"resolution": "720p",
"duration": 5,
"audio": false
}| Подсказка | Models compared in the same card | Fixed settings |
|---|---|---|
| Преобразование текста в видео в кинематографическом стиле | Wan 3.0 · Kling 3.0 · Seedance 2.5 · Grok Imagine · Happy Horse 1.0 · Veo 3.1 | 5 seconds · 720p · 16:9 · no audio |
| Согласованность характеров | Wan 3.0 · Kling 3.0 · Seedance 2.5 · Happy Horse 1.0 | 5 seconds · 720p · 16:9 · no audio |
| Complex motion and camera control | Wan 3.0 · Kling 3.0 · Grok Imagine · Veo 3.1 | 5 seconds · 720p · 16:9 · no audio |
| Dialogue and native audio | Wan 3.0 · Kling 3.0 · Seedance 2.5 · Grok Imagine | 5 seconds · 720p · 16:9 · native audio requested |
| Three-beat pottery story | Wan 3.0 · Kling 3.0 · Happy Horse 1.0 · Veo 3.1 | 15 seconds · 720p · 16:9 · native audio requested |
Six-Model Prompt Adherence Test Cards
ТЕСТИРОВАНИЕ ЗАВЕРШЕНО The five prompts below keep each comparison together. Every prompt has at least four models; the first cinematic prompt has all six. Returned duration is measured from each local MP4, so a route that ends early is described as returned rather than treated as a hidden quality score.
Prompt 1 · six models · 5s · 720p · 16:9
Преобразование текста в видео в кинематографическом стиле
Подсказка: A cinematic tracking shot follows a woman in a long red coat walking through a rain-soaked neon street at night. She turns toward the camera as a tram passes behind her, reflections moving naturally across the wet pavement. Realistic human motion, consistent face and clothing, shallow depth of field, controlled handheld camera, no cuts.
Wan 3.0 GLBGPT
Клинг 3.0 GLBGPT
Seedance 2,5 GLBGPT
Grok Imagine GLBGPT
Happy Horse 1.0 GLBGPT
Veo 3.1 GLBGPT · 5.04s
- Ван: следует за женщиной, показывает поворот и ведет трамвай по заднему плану.
- Клинг: keeps attractive rainy lighting, but the subject is comparatively static.
- Seedance 2.5: keeps the back-view walk, visible turn, and tram arrival in the requested order.
- Грок: retains the red coat, rain, neon street, and tram, with the tram clearly crossing behind the subject.
- Happy Horse: starts behind the woman, then brings the turn and tram into view with a tighter face shot.
- Veo: keeps the red coat and tram pass, but the camera is more static than the Wan and Seedance outputs.
Task observation: Seedance 2.5 has the clearest event-order match; Wan has the strongest camera/action coverage in this six-model card.
Prompt 2 · four models · 5s · 720p · 16:9
Сохранение стиля персонажа без эталонного изображения
Подсказка: One continuous shot of the same elderly watchmaker with round silver glasses, a green waistcoat, and a small scar above his left eyebrow. He picks up a pocket watch, walks from his workbench to the window, turns in profile, then faces the camera again. Keep his face, glasses, clothing, age, and scar unchanged throughout.
Wan 3.0 GLBGPT
Клинг 3.0 GLBGPT
Seedance 2,5 GLBGPT
Happy Horse 1.0 GLBGPT
- Wan and Kling: keep the watchmaker recognizable, but neither clearly preserves the eyebrow scar.
- Seedance 2.5: preserves the elderly watchmaker, glasses, waistcoat, and workbench-to-window movement; the scar is not clearly visible.
- Happy Horse: keeps the glasses and waistcoat through the profile-to-front movement; the defining scar is also not clear.
Task observation: all four preserve the main identity better than the smallest facial detail; no model-level winner assigned.
Prompt 3 · four models · 5s · 720p · 16:9
Сложное движение и управление камерой
Подсказка: A cyclist races downhill through a crowded Mediterranean market while the camera begins with a low-angle front tracking shot, arcs smoothly to his left side, and rises into a wide overhead view. Vendors step aside, fabric awnings move in the wind, oranges roll from a tipped basket, and every event happens in that order with realistic physics.
Wan 3.0 GLBGPT
Клинг 3.0 GLBGPT
Grok Imagine GLBGPT
Veo 3.1 GLBGPT
- Ван: показывает наиболее четкое движение переднего плана и переходит в вид сверху.
- Клинг: на этом изображении более наглядно показаны опрокинутая корзина и катящиеся апельсины.
- Грок: completes a readable front-to-side-to-overhead move and keeps the oranges visible.
- Veo: keeps the cyclist and market but lets the awnings dominate the later frames; the full event order is less clear.
Task observation: Wan and Grok show the strongest camera-path coverage; Kling is strongest on the orange detail.
Prompt 4 · four models · 5s · 720p · native audio requested
Диалоги и оригинальное звуковое сопровождение
Подсказка: Inside a quiet train compartment, a woman says, “We missed the last stop.” A man across from her replies, “Then we ride to the end.” Keep the two voices distinct, synchronize each line to the correct speaker, include subtle train ambience and wheel sounds, and do not add music.
Wan 3.0 GLBGPT
Клинг 3.0 GLBGPT
Seedance 2,5 GLBGPT
Grok Imagine GLBGPT
- All four returned train-compartment scenes with two visible speakers.
- Wan and Kling returned 5.04-second stereo AAC tracks in the saved records; the new Seedance and Grok files were inspected for scene coverage, but exact spoken-word and speaker assignment still need a controlled listening pass.
- Seedance presents the woman and man in separate views; Grok keeps both speakers in the same train composition for most frames.
Task observation: no winner assigned until the four audio tracks are transcribed and listened to under the same review protocol.
Prompt 5 · four models · 15s · 720p · native audio requested
История о керамике с мотивом «Три совпадающих элемента»
Подсказка: Create a coherent 15-second cinematic story in three connected beats: in a warm pottery studio, an adult artisan wearing a rust-colored apron shapes a tall blue vase on a spinning wheel; the same artisan carefully paints a thin white wave pattern around the same vase; the finished vase stands on a wooden table beside a small green plant while the artisan smiles in the background. Maintain the same adult artisan, rust-colored apron, blue vase, studio, and warm lighting throughout. Use natural scene transitions, realistic hand motion, soft pottery-wheel and brush sounds, and no narration or music.
Wan 3.0 GLBGPT
Клинг 3.0 GLBGPT
Happy Horse 1.0 GLBGPT
Veo 3.1 GLBGPT
- Ван: completes shaping, painting, and display, but broadens the requested thin wave into a wider decorative pattern.
- Клинг: keeps a recognizable white wave and the rust-colored apron more literally.
- Happy Horse: keeps the blue vase, white wave, and final plant display; the artisan framing changes between beats.
- Veo: completes the vase workflow and final display with a visible wave and plant; identity continuity is less stable than the object continuity.
Task observation: Kling is strongest on the literal white-wave detail; Happy Horse and Veo cover the three story beats clearly.
Какую модель выбрать?
Choose Wan 3.0 for:
- Ambitious camera movement
- Longer story coverage
- A strong starting point for motion-heavy prompts
Choose Kling 3.0 for:
- Literal visual details and patterns
- Character dialogue and lip-sync workflows
- Compact multi-shot scenes
Choose Seedance 2.5 for:
- Ordered cinematic events
- A visible turn and background action in one brief
- Prompts where event order matters more than a universal score
Try Grok, Happy Horse, or Veo when:
- You want a second camera interpretation
- You care about readable story beats and object coverage
- You can test the exact prompt before production
All six models were accessed through GLBGPT for this comparison. Run your own prompt in the same workspace, compare the returned cards, and choose the route that preserves the details your project actually needs.
Final Prompt-Adherence Verdict
No universal model winner. This six-model test gives task-level winners: Wan 3.0 for camera coverage, Kling 3.0 for the literal pottery pattern, and Seedance 2.5 for event order in the cinematic brief. Grok Imagine handled the market camera path well, Happy Horse 1.0 kept the character and pottery beats readable, and Veo 3.1 produced mixed execution outcomes across prompts. Those findings describe these prompts and runs, not every possible video brief.
Часто задаваемые вопросы
Which six AI video models did you test?
We tested Wan 3.0, Kling 3.0, Seedance 2.5, Grok Imagine, Happy Horse 1.0, and Veo 3.1 through GLBGPT.
Did every prompt use more than one model?
Yes. Every main prompt card contains at least four models, and the cinematic card contains all six. That keeps each prompt as the comparison unit instead of giving one model a separate prompt.
Which model followed the cinematic event order best?
Seedance 2.5 kept the red-coat walk, turn toward the camera, and tram arrival in the clearest requested order in this run. Wan 3.0 showed stronger camera and action coverage in the paired cinematic output.
Which model preserved fine visual details best?
Kling 3.0 rendered the pottery story’s thin white wave most literally. The result is a task-level observation, so a logo, costume, or line-art prompt should still be tested directly.
How did Veo 3.1 perform in this matrix?
Veo 3.1 returned a usable cinematic file and completed the visible motion and pottery beats, but the motion file was about four seconds and the pottery file about eight seconds even though longer durations were requested. Those shorter returns are recorded as execution details, not converted into a universal quality score.
Can these videos be used commercially?
Commercial-use rights depend on the current plan and terms attached to the access route. Review the live terms before publishing paid client work or advertising assets.
Sources and Test Evidence
- Higgsfield prompt-adherence comparison — reference for the same-prompt comparison idea.
- GLBGPT task records and local MP4 outputs — five prompt matrix, 720p, 16:9, with settings shown on each card.
- Local contact sheets — used to inspect event order, object continuity, camera movement, and visible detail across the returned clips.
