{"id":20083,"date":"2026-09-29T04:21:50","date_gmt":"2026-09-29T08:21:50","guid":{"rendered":"https:\/\/wp.glbgpt.com\/?p=20083"},"modified":"2026-09-29T04:49:18","modified_gmt":"2026-09-29T08:49:18","slug":"ai-video-prompt-adherence-comparison","status":"publish","type":"post","link":"https:\/\/wp.glbgpt.com\/ru\/hub\/ai-video-prompt-adherence-comparison","title":{"rendered":"\u0421\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0435 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u0432\u0438\u0434\u0435\u043e-\u043f\u0440\u043e\u043c\u043f\u0442\u043e\u0432 \u0418\u0418: 6 \u043c\u043e\u0434\u0435\u043b\u0435\u0439, \u043f\u0440\u043e\u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u043d\u0430 \u043e\u0434\u043d\u0438\u0445 \u0438 \u0442\u0435\u0445 \u0436\u0435 \u043f\u0440\u043e\u043c\u043f\u0430\u0445"},"content":{"rendered":"

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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\u0411\u044b\u0441\u0442\u0440\u044b\u0439 \u043e\u0442\u0432\u0435\u0442:<\/strong> There is no universal winner across all five prompts. Wan 3.0<\/strong> produced the clearest camera coverage in the motion tests, \u041a\u043b\u0438\u043d\u0433 3.0<\/strong> kept the pottery pattern most literally, and Seedance 2,5<\/strong> kept the cinematic walk, turn, and tram arrival in the clearest order. Grok Imagine<\/strong> was strong on the market camera path, Happy Horse 1.0<\/strong> kept the character and story beats readable, and Veo 3.1<\/strong> returned usable files for the cinematic, motion, and pottery prompts, with shorter returned durations on the latter two.<\/p>\n<\/div>\n\n\n\n

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\"\u041f\u0430\u043d\u0435\u043b\u044c<\/figure>\n\n\n\n
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\u041f\u043e\u043f\u0440\u043e\u0431\u0443\u0439\u0442\u0435 \u0431\u043e\u043b\u0435\u0435 100 \u043b\u0443\u0447\u0448\u0438\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043d\u0430 GlobalGPT<\/strong><\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n

What Is AI Video Prompt Adherence?<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

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\u2019s face, or stop before the final story beat. Each card therefore shows the exact prompt focus beside the model outputs and the observed misses.<\/p>\n\n\n\n

The Six Models in This Comparison<\/h2>\n\n\n\n

All six models use the same access label in this article: GLBGPT<\/strong>. The table is the complete lineup; there is no separate model section that introduces only two providers.<\/p>\n\n\n\n

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\u041c\u043e\u0434\u0435\u043b\u044c<\/th>\u0414\u043e\u0441\u0442\u0443\u043f<\/th>Prompt coverage in this comparison<\/th>What the cards test<\/th><\/tr><\/thead>
Wan 3.0<\/td>GLBGPT<\/td>All five prompts<\/td>Camera movement, characters, motion, audio, story order<\/td><\/tr>
\u041a\u043b\u0438\u043d\u0433 3.0<\/td>GLBGPT<\/td>All five prompts<\/td>Camera movement, characters, motion, audio, story order<\/td><\/tr>
Seedance 2,5<\/td>GLBGPT<\/td>Cinematic, character, dialogue<\/td>Event order, character detail, dialogue scene<\/td><\/tr>
Grok Imagine<\/td>GLBGPT<\/td>Cinematic, motion, dialogue<\/td>Camera path, action coverage, two-speaker scene<\/td><\/tr>
Happy Horse 1.0<\/td>GLBGPT<\/td>Cinematic, character, pottery<\/td>Character continuity and three-beat story coverage<\/td><\/tr>
Veo 3.1<\/td>GLBGPT<\/td>Cinematic request, motion, pottery<\/td>Route acceptance, camera movement, story beats<\/td><\/tr><\/tbody><\/table><\/figure>\n<\/div>\n\n\n\n

How We Tested the Six Models<\/h2>\n\n\n\n

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:<\/p>\n\n\n\n

{\n  \"surface\": \"GLBGPT\",\n  \"model\": \"seedance-2.5\",\n  \"prompt\": \"the identical test prompt\",\n  \"aspect_ratio\": \"16:9\",\n  \"resolution\": \"720p\",\n  \"duration\": 5,\n  \"audio\": false\n}<\/code><\/pre>\n\n\n\n
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\u041f\u043e\u0434\u0441\u043a\u0430\u0437\u043a\u0430<\/th>Models compared in the same card<\/th>Fixed settings<\/th><\/tr><\/thead>
\u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0442\u0435\u043a\u0441\u0442\u0430 \u0432 \u0432\u0438\u0434\u0435\u043e \u0432 \u043a\u0438\u043d\u0435\u043c\u0430\u0442\u043e\u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u043e\u043c \u0441\u0442\u0438\u043b\u0435<\/td>Wan 3.0 \u00b7 Kling 3.0 \u00b7 Seedance 2.5 \u00b7 Grok Imagine \u00b7 Happy Horse 1.0 \u00b7 Veo 3.1<\/td>5 seconds \u00b7 720p \u00b7 16:9 \u00b7 no audio<\/td><\/tr>
\u0421\u043e\u0433\u043b\u0430\u0441\u043e\u0432\u0430\u043d\u043d\u043e\u0441\u0442\u044c \u0445\u0430\u0440\u0430\u043a\u0442\u0435\u0440\u043e\u0432<\/td>Wan 3.0 \u00b7 Kling 3.0 \u00b7 Seedance 2.5 \u00b7 Happy Horse 1.0<\/td>5 seconds \u00b7 720p \u00b7 16:9 \u00b7 no audio<\/td><\/tr>
Complex motion and camera control<\/td>Wan 3.0 \u00b7 Kling 3.0 \u00b7 Grok Imagine \u00b7 Veo 3.1<\/td>5 seconds \u00b7 720p \u00b7 16:9 \u00b7 no audio<\/td><\/tr>
Dialogue and native audio<\/td>Wan 3.0 \u00b7 Kling 3.0 \u00b7 Seedance 2.5 \u00b7 Grok Imagine<\/td>5 seconds \u00b7 720p \u00b7 16:9 \u00b7 native audio requested<\/td><\/tr>
Three-beat pottery story<\/td>Wan 3.0 \u00b7 Kling 3.0 \u00b7 Happy Horse 1.0 \u00b7 Veo 3.1<\/td>15 seconds \u00b7 720p \u00b7 16:9 \u00b7 native audio requested<\/td><\/tr><\/tbody><\/table><\/figure>\n<\/div>\n\n\n\n
Six-Model Prompt Adherence Test Cards<\/h2>\n\n\n\n
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\u0422\u0415\u0421\u0422\u0418\u0420\u041e\u0412\u0410\u041d\u0418\u0415 \u0417\u0410\u0412\u0415\u0420\u0428\u0415\u041d\u041e<\/span> 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.<\/p>\n<\/div>\n\n\n\n
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Prompt 1 \u00b7 six models \u00b7 5s \u00b7 720p \u00b7 16:9<\/span><\/p>\n\n\n\n
\u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0442\u0435\u043a\u0441\u0442\u0430 \u0432 \u0432\u0438\u0434\u0435\u043e \u0432 \u043a\u0438\u043d\u0435\u043c\u0430\u0442\u043e\u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u043e\u043c \u0441\u0442\u0438\u043b\u0435<\/h3>\n<\/div>\n\n\n\n
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\u041f\u043e\u0434\u0441\u043a\u0430\u0437\u043a\u0430:<\/strong> 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.<\/p>\n<\/div>\n\n\n\n
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Wan 3.0<\/strong> GLBGPT<\/span><\/p>\n\n\n\n