Cosmos Predict 2.5 Text To Video
Playground
Try it on WavespeedAI!Cosmos Predict 2.5 Text-to-Video generates video from text prompts using NVIDIA’s 2B Cosmos Post-Trained Model. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.
Features
Cosmos Predict 2.5 Text-to-Video
Cosmos Predict 2.5 Text-to-Video generates video from text prompts using NVIDIA’s 2B Cosmos Post-Trained Model. Describe a scene in natural language — the model creates smooth, cinematic video clips with realistic motion, lighting, and atmospheric effects.
Why Choose This?
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NVIDIA Cosmos architecture Powered by NVIDIA’s 2B parameter Cosmos Post-Trained Model for high-quality video generation.
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Pure text-to-video Generate videos from text descriptions alone — no reference images required.
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Cinematic quality Produces realistic scenes with natural lighting, motion, and environmental effects.
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Prompt Enhancer Built-in tool to automatically improve your scene descriptions.
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Simple pricing Flat $0.25 per video, no complex calculations.
Parameters
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the desired scene and motion |
How to Use
- Write your prompt — describe the scene, characters, motion, and atmosphere in detail.
- Use Prompt Enhancer (optional) — click to automatically refine your description.
- Run — submit and download your generated video.
Pricing
| Output | Cost |
|---|---|
| Per video | $0.25 |
Best Use Cases
- Cinematic Scenes — Generate atmospheric street scenes, landscapes, and urban environments.
- Storytelling — Create visual narratives from written descriptions.
- Concept Visualization — Bring creative ideas to life without reference images.
- Social Media Content — Produce engaging short-form videos from text.
- Marketing & Ads — Generate promotional video content quickly.
Pro Tips
- Be specific and descriptive — include details about characters, actions, environment, lighting, and weather.
- Use cinematic language like “autumn leaves falling,” “rainy street,” or “golden hour lighting” for atmospheric results.
- Describe camera movement if desired (e.g., “tracking shot,” “slow pan”).
- Try the Prompt Enhancer to automatically improve your descriptions.
- Include mood and atmosphere details for more evocative results.
Notes
- Prompt is the only required field.
- Ensure your prompts comply with content guidelines.
Related Models
- Cosmos Predict 2.5 Image-to-Video — Generate video from images.
- Cosmos Predict 2.5 Video-to-Video — Transform existing videos with text prompts.
- Wan 2.6 Text-to-Video — Multi-resolution text-to-video with flexible duration.
Authentication
For authentication details, please refer to the Authentication Guide.
API Endpoints
Submit Task & Query Result
# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/cosmos-predict-2.5/text-to-video" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| prompt | string | Yes | - | Text prompt describing the scene, action, and visual style you want in the generated video |
Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Array of URLs to the generated content (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction, the ID of the prediction to get |
| data.model | string | Model ID used for the prediction |
| data.outputs | string | Array of URLs to the generated content (empty when status is not completed). |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |