# Grok Imagine Video v1.5 Image-to-Video — Atlas Cloud API

> xAI Grok Imagine Video v1.5 animates a starting frame image with natural-language motion prompts at 480p/720p/1080P.

This is the machine-readable API reference for **Grok Imagine Video v1.5 Image-to-Video** on Atlas Cloud,
a unified API platform for 400+ AI models across text, image, video, audio and 3D.

- **Model ID**: `xai/grok-imagine-video-v1.5/image-to-video`
- **Built by**: xAI
- **Modality**: Video
- **Model page**: https://www.atlascloud.ai/models/xai/grok-imagine-video-v1.5/image-to-video
- **API key**: https://www.atlascloud.ai/console/api-keys
- **Docs**: https://www.atlascloud.ai/docs

## Pricing on Atlas Cloud

- $0.08 per second of generated video
- Pay-as-you-go. No minimum spend, no subscription required.

> **These are the authoritative Atlas Cloud rates for this model.** Any price that
> appears in the vendor description further down refers to a different platform or
> a different model variant and does not apply here.

## Use this model from an AI agent

Atlas Cloud ships three first-party integration surfaces. All three authenticate
with the same API key via the `ATLASCLOUD_API_KEY` environment variable.

### MCP server

The official MCP server (`atlascloud-mcp`) exposes this model to any
MCP-compatible host — Claude Code, OpenAI Codex, Cursor, Gemini CLI, Goose,
Claude Desktop. One-line install:

```bash
# Claude Code
claude mcp add atlascloud -- npx -y atlascloud-mcp

# OpenAI Codex CLI
codex mcp add atlascloud -- npx -y atlascloud-mcp

# Gemini CLI
gemini mcp add atlascloud -- npx -y atlascloud-mcp

export ATLASCLOUD_API_KEY="your-api-key"
```

Then ask in plain English; the agent calls `atlas_generate_video` with `model: "xai/grok-imagine-video-v1.5/image-to-video"`.
The server fetches each model's schema and validates parameters before submitting,
so invalid requests fail fast without spending credits.

MCP docs: https://www.atlascloud.ai/docs/mcp-server

### Agent Skills

`atlas-cloud-skills` is a portable skill package (API reference, code templates in
Python / Node.js / cURL, model IDs with pricing) for Claude Code, Cursor, Codex and
12+ other agents:

```bash
npx skills add AtlasCloudAI/atlas-cloud-skills
export ATLASCLOUD_API_KEY="your-api-key"
```

Skills docs: https://www.atlascloud.ai/docs/skills

### CLI

The `atlas` binary runs Atlas Cloud from a terminal or CI script. Async media jobs
are polled and downloaded automatically (use `--no-download` when a script only
needs the output URLs):

```bash
# Install (Homebrew, npm, or shell installer)
brew install AtlasCloudAI/tap/atlascloud
# npm install -g atlascloud-cli
# curl -fsSL https://raw.githubusercontent.com/AtlasCloudAI/cli/main/install.sh | sh

atlas auth login
atlas generate video xai/grok-imagine-video-v1.5/image-to-video -p "Your prompt here"
```

CLI docs: https://www.atlascloud.ai/docs/cli

## HTTP API reference

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateVideo` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `xai/grok-imagine-video-v1.5/image-to-video`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  Model name.
  - Default: `"xai/grok-imagine-video-v1.5/image-to-video"`

- **`prompt`** (`string`, _required_):
  Natural-language motion prompt. The starting frame is taken from the image.

- **`image_url`** (`string`, _required_):
  Public HTTPS URL or base64 data URI of the starting-frame image (JPEG, PNG, or WebP).

- **`duration`** (`integer`, _optional_):
  Length of generated video in seconds. Range: 1–15.
  - Default: `8`
  - Min: 1
  - Max: 15

- **`resolution`** (`string`, _optional_):
  Output resolution.
  - Default: `"720p"`
  - Options: "480p", "720p", "1080p"

- **`aspect_ratio`** (`string`, _optional_):
  Output aspect ratio. The default matches the input image; specifying a different value stretches the image.
  - Default: `"16:9"`
  - Options: "1:1", "16:9", "9:16", "4:3", "3:4", "3:2", "2:3"



**Required Parameters Example**:

```json
{
  "model": "xai/grok-imagine-video-v1.5/image-to-video",
  "prompt": "",
  "image_url": ""
}
```


**Full Example**:

```json
{
  "model": "xai/grok-imagine-video-v1.5/image-to-video",
  "prompt": "",
  "image_url": "",
  "duration": 8,
  "resolution": "720p",
  "aspect_ratio": "16:9"
}
```


### Output Schema

The API returns the following output format:


- **`id`** (`string`, _optional_):
  Unique identifier for the prediction.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated video (empty when status is not completed).

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created.



**Example Response**:

```json
{
  "id": "",
  "urls": {},
  "model": "",
  "status": "",
  "outputs": [
    ""
  ],
  "created_at": ""
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateVideo" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "xai/grok-imagine-video-v1.5/image-to-video",
  "prompt": "",
  "image_url": "",
  "duration": 8,
  "resolution": "720p",
  "aspect_ratio": "16:9"
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/xai/grok-imagine-video-v1.5/image-to-video)

## About this model

_Vendor-supplied description. Any pricing or endpoint mentioned below refers to_
_other platforms — use the Atlas Cloud values above._

#### 1. Introduction

**Grok Imagine Video V1.5** is a frontier-tier image-to-video generation model developed by xAI that animates static images into short clips of up to 15 seconds with natively generated, synchronized audio — including dialogue, lip-sync, sound effects, and ambient music — produced in a single inference pass.

This README applies to the following API model identifier:

- `xai/grok-imagine-video-v1.5/image-to-video`

Released in preview around late May 2026, Grok Imagine Video V1.5 debuted at the top of the [Artificial Analysis Video Arena Image-to-Video leaderboard](https://deevid.ai/blog/grok-imagine-video-review) with a 1404 ±6 Elo rating, surpassing ByteDance Seedance 2.0 and other established competitors. Built on xAI's Aurora engine — an autoregressive mixture-of-experts (MoE) network that jointly models text, image, video, and audio tokens — the model represents a departure from the diffusion-transformer paradigm used by Sora and Veo, enabling tightly coupled audiovisual generation with competitive cost and latency characteristics.

---

#### 2. Key Features

- **Native Synchronized Audio Generation**: Audio (dialogue, lip-sync, SFX, ambient sound, music) is generated jointly with video tokens in a single inference pass rather than dubbed in post-processing. This produces event-aligned sound effects and natural lip-sync without requiring separate audio pipelines.

- **Aurora Autoregressive MoE Architecture**: Unlike diffusion-transformer competitors, V1.5 uses an autoregressive mixture-of-experts network trained to predict next tokens from interleaved multimodal data. This unified token-space approach is what enables single-pass audio-video coherence.

- **Granular Duration Control (1–15 seconds)**: Clips can be requested at any integer second from 1 to 15, supporting precise targeting for short-form formats. V1.5 extends the prior 10-second limit by 50% while maintaining temporal coherence across the longer window.

_(Description truncated. Full text on the model page.)_

---

Atlas Cloud — one API for 400+ AI models. Model page: https://www.atlascloud.ai/models/xai/grok-imagine-video-v1.5/image-to-video · Docs: https://www.atlascloud.ai/docs