Best AI Video APIs I'm using as a developer in 2026

Building an AI video application is easier now, with more providers offering text-to-video, image-to-video, and video-editing APIs.

But choosing one can still be difficult when every model has different inputs, limits, and pricing.

Some APIs can:

• generate videos with audio, while

• others give you more control over frames, references, and editing.

You should also consider generation time, per-request cost, and whether you need to manage multiple provider accounts.

So I reviewed the official documentation, available models, and developer SDKs to find 9 AI video API platforms to consider in October 2026.

Here's what each one offers, when to use it, and how to get started.

TL;DR: Which AI video API should you choose?

A few defaults I always look up to based on the task:

• Composio Instant: When building AI agents that need video generation alongside other tools, without managing separate provider API keys.

• Google Gemini API: When generating videos from text or images and refining them through follow-up prompts.

• Runway API: When building production workflows that involve video generation and task management.

• fal.ai: When accessing multiple video models, such as Kling for multi-shot videos, Seedance for longer clips, and MiniMax H3 for motion and audio.

• Alibaba Wan 3.0: Use this when generating videos with multiple references, frame controls, or editing requirements.

These defaults work well for most tasks, but if you are building a custom workflow, I recommend trying the other option once.

Best AI video APIs at a glance

Here's a quick comparison before getting into the installation details.

The models have different duration, resolution, and audio limits.

Their prices can also change with the selected settings, so check the linked pricing documentation before calculating production costs.

How I selected these APIs

I looked at five things:

• supported generation modes,

• model capabilities,

• developer access,

• installation process, and

• practical use cases.

I also looked at access restrictions and how developers submit requests, retrieve results, and manage generated files.

Best AI Video APIs

With those differences in mind, here's what each API does and where it makes sense to use it.

1. Composio Instant

Say you're building an agent that needs to generate a product video, save it to Google Drive, and prepare it for publishing.

You can call a video provider directly, but then you need to manage its API key, billing, and authentication alongside your other tools.

Composio Instant handles that setup for supported paid tools.

Your agent can use eligible video-generation actions through Composio-managed provider accounts, and your organisation's prepaid balance covers usage.

The platform provides Instant-enabled media-generation tools through Higgsfield, Luma Labs, Google Gemini (Veo and Nano Banana), Black Forest Labs (FLUX), fal.ai, MiniMax, PhotoRoom, ElevenLabs, Fish Audio, and Deepgram, covering supported video, image, and voice-generation actions.

Higgsfield also exposes Seedance 2.5 generation actions, so agents can combine video generation with other supported tools and connected applications.

Best for: Anyone building AI agents that need video generation as part of a larger workflow.

When to use

Composio Instant is useful when your agent needs to work across several tools, not just generate a video.

• Build agents that generate videos from text prompts or images.

• Test supported paid video tools without managing separate provider API keys.

• Combine video generation with storage, research, or publishing workflows.

• Give MCP-compatible agents access to supported generation actions.

• Track Instant tool charges through Composio's execution data.

How to install

Step 1. Create an account at Composio and generate an API key.

Step 2. Install the Python SDK.

pip install composio

For TypeScript:

npm install @composio/core

Step 3. Configure your API key.

export COMPOSIO_API_KEY="your_api_key"

Step 4. Open your Composio project settings, enable Instant Toolkits, and check the prepaid balance under Billing.

Step 5. Create a session that allows the video toolkits.

from composio import Composio

<p>composio = Composio()</p>
<p>session = composio.sessions.create(
    user_id="user_123",
    toolkits=["higgsfield", "luma_labs"],
    instant={
        "toolkits": {
            "enable": ["higgsfield", "luma_labs"]
        },
        "return_instant_charge": True,
    },
)</p>
<p>tools = session.tools()</p>

This creates a session through which your agent can discover and execute permitted tools. The agent still needs to select an eligible video-generation action and provide its required inputs.

Note: Instant is marked as experimental and is disabled by default at the project level. Access may also require approval.

Learn More: Composio Instant | Instant documentation | Higgsfield toolkit | Luma Labs toolkit

Composio is useful when video generation is part of an agent workflow.

But if you want to work directly with a particular video model and control its generation settings, Google's API is another option.

2. Google Gemini API

Google provides video generation through the Gemini API, including Gemini Omni Flash 1.1 and the Veo 3.1 preview models.

Gemini Omni Flash supports text-to-video, image-to-video, and conversational editing through the Interactions API.

You can generate a video, then send another instruction to change specific parts while continuing from the previous interaction.

For example:

you could generate a short product scene and then ask the model to change its lighting or camera perspective.

Gemini Omni Flash also supports video extension, first-and-last-frame guidance, and resolution settings.

Best for: Applications that need to generate a video and then refine it through follow-up instructions.

When to use

Gemini works well when your application requires generation and repeated editing.

• Generate short marketing or product videos.

• Animate images and illustrations.

• Edit an existing generation through follow-up prompts.

• Create portrait videos for mobile applications.

• Extend clips or guide transitions between reference frames.

How to install

Step 1. Create an API key at Google AI Studio.

Step 2. Install the official Google Gen AI SDK.

pip install google-genai

For JavaScript:

npm install @google/genai

Step 3. Set your API key.

export GEMINI_API_KEY="your_api_key"

Step 4. Generate a video using Gemini Omni Flash.

import base64
from google import genai<p></p>
<p>client = genai.Client()</p>
<p>interaction = client.interactions.create(
    model="gemini-omni-1.1-flash",
    input=(
        "A red sports car driving along a coastal road "
        "at sunset. The camera follows from behind."
    ),
)</p>
<p>with open("video.mp4", "wb") as file:
    file.write(
        base64.b64decode(
            interaction.output_video.data
        )
    )</p>

This sends a generation request and saves the video as an MP4 file.

For conversational editing, the Interactions API supports previous_interaction_id, which lets you continue from an earlier generation.

Note: Google released gemini-omni-1.1-flash as a generally available model on. Its 1080p and 4K output settings use upscaling.

Learn More: Gemini Omni Flash documentation | Gemini API release notes | Veo documentation | Model deprecations

Gemini is useful when you want to edit a generated video through conversation.

But if you're adding video generation to a larger production application, Runway provides SDKs and task-management workflows.

3. Runway API

Runway provides a developer API for video generation with models such as Gen-4.5 and Gen-4 Turbo. It also supports selected video models from other providers through the same platform.

You can submit text-to-video and image-to-video requests, then retrieve their results using the official Python or JavaScript SDK.

Runway also provides documented recipes for common production tasks, including product advertisements, product replacement, and multi-shot videos.

Runway is useful when your application needs to generate assets and pass them into other production steps. You can manage the generation task, retrieve the output, and continue processing the video.

Best for: Teams building structured video-generation and production applications.

When to use

Runway is useful when video generation needs to fit into a larger production process.

• Generate advertising and product videos.

• Animate existing product photography.

• Build asynchronous video-generation applications.

• Access supported third-party models through Runway.

• Use documented recipes for common creative tasks.

How to install

Step 1. Create an account at Runway Dev.

Step 2. Generate an API key.

Step 3. Install the official SDK.

pip install runwayml

For JavaScript:

npm install @runwayml/sdk

Step 4. Configure your key.

export RUNWAYML_API_SECRET="your_api_key"

Step 5. Generate a video from an image.

from runwayml import RunwayML<p></p>
<p>client = RunwayML()</p>
<p>task = client.image_to_video.create(
    model="gen4.5",
    prompt_image=(
        "<a href="https://upload.wikimedia.org/wikipedia/">https://upload.wikimedia.org/wikipedia/</a>"
        "commons/8/85/Tour_Eiffel_Wikimedia_Commons_"
        "(cropped).jpg"
    ),
    prompt_text=(
        "A timelapse on a sunny day "
        "with clouds flying by."
    ),
    ratio="1280:720",
    duration=5,
).wait_for_task_output()</p>
<p>print(task)</p>

This follows Runway's documented image_to_video.create() method. The SDK waits for the task output and returns the result when processing finishes.

Note: Runway uses a credit-based billing system, and model costs differ. Generation settings and supported operations also vary between models.

The SDK provides methods for managing generation tasks, but you should still test failures, timeouts, and output handling before using the API in production.

Learn More: Runway API getting started | Runway models | Runway pricing

Runway provides access to several video models through its platform.

If you want to compare models such as Kling, Seedance, and MiniMax without managing separate integrations, Fal offers another approach.

4. fal.ai

fal.ai provides hosted APIs for video models from several providers. Its catalogue includes Kling, ByteDance Seedance, MiniMax Hailuo, Alibaba Wan, Google models, and others.

You can use the same client library and API key to call these models, although each endpoint has its own supported inputs and parameters.

This makes it useful when you want to compare outputs or let users choose a model without building a separate authentication integration for each provider.

fal.ai also supports queued requests, status checks, and webhooks, which are useful for video generation because requests can take time, and your application may need to continue other work while a video processes.

Best for: Developers testing multiple video models or building applications that need several generation options.

Which video models can you use through fal?

Here are three models that meet different needs and are available through fal's documented endpoints.

• Kling 4.0: For multi-shot videos and frame controls:

• Seedance 2.5: For longer clips and reference-heavy generation

• MiniMax Hailuo H3: For motion and native audio

When to use

Fal is useful when you want to experiment with models that solve different video-generation problems.

• Use Kling for multi-shot sequences, product videos, and frame guidance.

• Use Seedance for longer clips and workflows with several reference inputs.

• Use MiniMax H3 for motion-focused videos and native audio.

• Compare different models using one API key.

• Build applications that let users select a video-generation model.

• Use asynchronous jobs and webhooks for video-generation tasks.

How to install

You only need to install the fal client once. After that, you can select a supported video model by changing the endpoint and its required arguments.

Step 1. Create an account at fal.ai and generate an API key.

Step 2. Install the official client.

For Python:

pip install fal-client

For JavaScript:

npm install @fal-ai/client

Step 3. Configure your API key.

export FAL_KEY="your_api_key"

Step 4. Choose a model and submit a generation request.

You can use fal's Python client to call these endpoints. For example:

import fal_client<p></p>
<p>result = fal_client.subscribe(
    "minimax/h3/text-to-video",
    arguments={
        "prompt": (
            "A mountain landscape at sunrise. "
            "The camera moves above the clouds."
        ),
        "duration": 5,
    },
)</p>
<p>print(result["video"]["url"])</p>

Here, the minimax/h3/text-to-video is the model id to be used

Step 5. Handle generation results.

The fal.subscribe() method waits for the result and returns the generated output when the request finishes.

For production applications, fal also provides queue submission and result-retrieval methods.

Use those methods when you don't want your application to keep a request open during generation.

Note: Every fal endpoint has its own input schema, supported settings, and pricing. You cannot assume that a parameter supported by Kling will also work with Seedance or MiniMax. Also, Kling 4.0 may require early-access approval, while Seedance 2.5 has additional access requirements for some B2B integrations, including an end_user_id field.

Learn More: fal documentation | Video-generation API overview | Kling 4.0 API | Seedance 2.5 API | MiniMax H3 API | fal pricing

fal makes it easier to work with different video models through one client.

But if you prefer first-party access to Alibaba's Wan models, Model Studio provides its own API and task-management controls.

5. Alibaba Cloud Model Studio

Alibaba's Wan 3.0 supports: text-to-video, image-to-video, and reference-based video generation. It supports clips up to 30 seconds long, with a standard model and a faster Prime variant.

You can provide starting and ending frames or use reference images, video, and audio to guide generation. The API also supports video editing and extension through documented reference-media inputs.

Wan uses asynchronous tasks:

• You submit a generation request,

• receive a task ID, and

• poll for the result.

This provides application control over when it checks the job and retrieves the completed video.

Best for: Developers who want direct access to a video model with several reference and editing options.

When to use

Wan 3.0 is useful when you need flexible generation inputs and task management.

• Generate product and promotional videos.

• Control starting and ending frames.

• Use reference media to guide a scene.

• Edit or extend video content.

• Build video services using Alibaba Cloud.

How to install

Wan 3.0 supports direct HTTP requests, so you can start using cURL.

Step 1. Create an Alibaba Cloud Model Studio account.

Step 2. Enable Wan 3.0 in a supported region and obtain your API key and workspace ID.

Step 3. Configure your credentials.

export DASHSCOPE_API_KEY="your_api_key"
export WAN_WORKSPACE_ID="your_workspace_id"

Step 4. Submit a generation request.

This example uses the Singapore regional endpoint.

curl -X POST <br />  "https://${WAN_WORKSPACE_ID}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/video-generation/video-synthesis" <br />  -H "Authorization: Bearer $DASHSCOPE_API_KEY" <br />  -H "X-DashScope-Async: enable" <br />  -H "Content-Type: application/json" <br />  -d '{
    "model": "wan3.0-video",
    "input": {
      "prompt": "A red bicycle moving along a quiet road at sunrise."
    },
    "parameters": {
      "resolution": "480P",
      "ratio": "adaptive",
      "duration": 5
    }
  }'

Step 5. Use the returned task ID to check the result.

curl <br />  "https://${WAN_WORKSPACE_ID}.ap-southeast-1.maas.aliyuncs.com/api/v1/tasks/YOUR_TASK_ID" <br />  -H "Authorization: Bearer $DASHSCOPE_API_KEY"

Replace YOUR_TASK_ID with the ID returned by the creation request.

When generation finishes, the API returns a video URL that you can use to download the result.

Note: The task ID and completed video URL are valid for 24 hours, so save the generated file to your own storage when you need to keep it.

Learn More: Wan 3.0 API reference | Wan 3.0 generation guide

Wan provides different ways to guide a generated scene.

But if you're starting with an image and want to animate or edit it, Luma provides a dedicated API for those workflows.

6. Luma API

Luma generates video with its Ray 3.2 model. Developers can use the Luma Agents API for text-to-video, image-to-video, editing, and reframing.

Ray 3.2 also supports ending-frame guidance, which is useful when a video needs to transition toward a specific image.

Luma also uses an asynchronous API: you submit a generation request → poll its status, then retrieve the video. Official SDKs are available for Python, TypeScript, and Go.

Best for: Image-driven video applications and workflows that also need editing or reframing.

When to use

Luma is useful when your application already works with images or video assets.

• Animate product photography.

• Generate short videos from text.

• Create transitions between images.

• Edit or reframe existing footage.

• Add image-to-video generation to creative applications.

How to install

Step 1. Create a Luma API account and obtain your API key.

Step 2. Install the Luma Agents SDK.

pip install luma-agents

For TypeScript:

npm install luma-agents

Step 3. Configure the key.

export LUMA_AGENTS_API_KEY="your_api_key"

Step 4. Generate a video.

import time
from luma_agents import Luma<p></p>
<p>client = Luma()</p>
<p>generation = client.generations.create(
    model="ray-3.2",
    type="video",
    prompt=(
        "A slow camera movement through "
        "a greenhouse at sunrise."
    ),
    aspect_ratio="16:9",
    video={
        "resolution": "720p",
        "duration": "5s",
    },
)</p>
<p>while generation.state not in ("completed", "failed"):
    time.sleep(2)
    generation = client.generations.get(
        generation.id
    )</p>
<p>if generation.state == "completed":
    print(generation.output[0].url)
else:
    print(generation.failure_reason)</p>

The script submits a generation request and checks the task until it completes or fails.

For image-to-video, use the documented start-frame and end-frame fields with supported image references.

Note: The API returns completed video URLs. Save the files to your own storage if you need to retain them for later workflows. Also requests are asynchronous, so applications need to handle polling, failures, and output storage.

Learn More: Luma API | Luma Agents quickstart | Luma API reference

Luma works well when you want to use its own model.

But if you already use OpenRouter for other AI models, its video API lets you add generation without setting up a separate platform.

7. OpenRouter

OpenRouter provides access to models from several providers through one account.

It introduced a dedicated video-generation API that supports asynchronous text-to-video and image-to-video requests.

You can:

submit a generation job → receive an ID → check the status → download the result.

OpenRouter also provides common request settings for duration, resolution, aspect ratio, and reference images.

Its model comparison lists video-generation options from providers including:

• Google,

• ByteDance,

• Alibaba,

• MiniMax,

• Runway, and

• xAI.

This makes it useful for developers who want video generation alongside existing language-model integrations.

Best for: Applications that already use OpenRouter or need video generation from several supported models.

When to use

OpenRouter is useful when you want to manage multiple AI models through one integration.

• Add video generation to an existing OpenRouter application.

• Compare supported video models.

• Let users choose between available generation models.

• Manage asynchronous video jobs.

• Review model-specific settings and pricing.

How to install

OpenRouter supports direct HTTP requests, so you can start using cURL.

Step 1. Create an OpenRouter account and generate an API key.

Step 2. Configure your key.

export OPENROUTER_API_KEY="your_api_key"

Step 3. Submit a video-generation request.

curl -X POST <br />  <a href="https://openrouter.ai/api/v1/videos">https://openrouter.ai/api/v1/videos</a> <br />  -H "Authorization: Bearer $OPENROUTER_API_KEY" <br />  -H "Content-Type: application/json" <br />  -d '{
    "model": "alibaba/wan-3.0",
    "prompt": "A small robot walking through a garden."
  }'

The response returns a job ID and its status.

Step 4. Poll for the result.

curl <br />  "<a href="https://openrouter.ai/api/v1/videos/YOUR_JOB_ID">https://openrouter.ai/api/v1/videos/YOUR_JOB_ID</a>" <br />  -H "Authorization: Bearer $OPENROUTER_API_KEY"

When the job completes, the response includes available content URLs.

Step 5. Download the finished video.

curl <br />  "<a href="https://openrouter.ai/api/v1/videos/YOUR_JOB_ID/content?index=0">https://openrouter.ai/api/v1/videos/YOUR_JOB_ID/content?index=0</a>" <br />  -H "Authorization: Bearer $OPENROUTER_API_KEY" <br />  --output video.mp4

Replace YOUR_JOB_ID with the job identifier returned by the creation request.

Note: Completed videos also require authenticated downloads, so your application needs to pass the API key when retrieving the file.

Learn More: OpenRouter video API announcement | Video-generation documentation | October 15 model comparison

OpenRouter provides a common API for several video models. Higgsfield also offers multi-model access, and additional creative operations such as motion transfer.

8. Higgsfield API

Higgsfield provides APIs for image and video generation. Its platform includes access to different video models alongside creative operations such as motion transfer.

One example is Higgsfield's Genjutsu motion-transfer endpoint. You provide a source video and a reference image, and the API creates a generation task using those inputs.

Higgsfield uses asynchronous requests. This means your application can check the generation status and retrieve the output when processing finishes

Best for: Creative applications that need model-specific operations such as motion transfer.

When to use

Higgsfield is useful when your application requires specific generation or editing controls.

• Create videos using supported generation models.

• Apply motion from existing footage to a reference image.

• Build social content or advertising tools.

• Use image-to-video and reference-based operations.

• Manage creative generation tasks through an API.

How to install

Higgsfield supports direct HTTP requests and official SDKs.

Step 1. Create an account at Higgsfield and generate API credentials.

Step 2. Install the TypeScript SDK if you want to use it.

npm install @higgsfield/client

Step 3. Configure your API credentials.

export HF_API_KEY_ID="your_key_id"
export HF_API_KEY_SECRET="your_key_secret"

Step 4. Submit a motion-transfer request.

curl -X POST <br />  <a href="https://api.higgsfield.ai/higgsfield/genjutsu/motion-transfer/v1.0">https://api.higgsfield.ai/higgsfield/genjutsu/motion-transfer/v1.0</a> <br />  -H "Authorization: Key ${HF_API_KEY_ID}:${HF_API_KEY_SECRET}" <br />  -H "Content-Type: application/json" <br />  -d '{
    "video_url": "<a href="https://example.com/input.mp4">https://example.com/input.mp4</a>",
    "image_urls": [
      "<a href="https://example.com/input.jpg">https://example.com/input.jpg</a>"
    ]
  }'

Replace the example URLs with accessible input files. The documented Genjutsu operation requires a source video of at least four seconds.

The request returns an identifier and a status URL to check the generation.

Note: generated output is available for at least seven days, so download finished files to your own storage if you need long-term access.

Learn More: Higgsfield API documentation | Higgsfield video models | Higgsfield API status

Higgsfield offers several creative operations through a single platform. The final API is worth considering when your application needs generated dialogue and control over specific frames.

9. Black Forest Labs FLUX 3 Video

Black Forest FLUX 3 Video is a multimodal video-generation model from Black Forest Labs.

It generates video and synchronised audio in the same request, including speech with lip-sync, sound effects, and background ambience.

You can generate a video from text, animate reference images, or continue an existing clip.

It supports up to ten keyframes, which you can assign to specific timestamps, so you can guide what appears at different points in the video.

FLUX 3 Video generates clips from 5 to 20 seconds at 24 fps, with HD, FHD, QHD, and UHD output options.

Best for: Developers who need synchronised audio, frame-guided generation, video continuation, and draft-based iteration.

When to use

FLUX 3 Video is useful when you need to control the visual and audio elements of a generated clip.

• Generate videos with speech, lip-sync, sound effects, and ambience in one request.

• Create videos using starting frames, ending frames, or multiple timestamped keyframes.

• Continue an existing clip while guiding the next scene with a prompt.

• Generate videos that include titles, signs, or other on-screen typography.

• Test different ideas using draft mode before generating a full-quality version.

How to install

FLUX 3 Video provides a REST API, so you can use it without installing a dedicated model SDK.

Step 1. Create an account at Black Forest Labs and generate an API key.

Step 2. Install the Python HTTP client if you don't already have it using pip install requests.

Step 3. Configure your API key using export BFL_API_KEY="your_api_key".

Step 4. Submit a video-generation request.

You can generate an eight-second video with synchronised audio and check the result until processing finishes.

import os
import time
import requests<p></p>
<p>api_key = os.environ["BFL_API_KEY"]</p>
<p>headers = {
    "x-key": api_key,
    "Content-Type": "application/json",
}</p>
<p>response = requests.post(
    "<a href="https://api.bfl.ai/v1/flux-3-video">https://api.bfl.ai/v1/flux-3-video</a>",
    headers=headers,
    json={
        "mode": "t2v",
        "prompt": "A fox running through mist at sunrise.",
        "duration": 8,
        "resolution": "hd",
        "generate_audio": True,
        "draft": False,
    },
)</p>
<p>response.raise_for_status()
job = response.json()</p>
<p>while True:
    time.sleep(2)</p>
<pre><code>response = requests.get(
    job["polling_url"],
    headers={"x-key": api_key},
)

response.raise_for_status()
result = response.json()

if result["status"] == "Ready":
    print(result["result"]["sample"])
    break

if result["status"] in (
    "Error",
    "Request Moderated",
    "Content Moderated",
):
    raise RuntimeError(result["status"])</code></pre><p dir="auto">The API submits the request asynchronously and returns a <code>polling_url</code>. The script checks that URL until the video is ready and then prints the generated MP4 URL.</p><p dir="auto">FLUX 3 Video supports these modes through the same endpoint:</p><ul dir="auto"><li data-preset-tag="p"><p><strong>Text-to-video (</strong><code><strong>t2v</strong></code><strong>):</strong> Generate a clip using a written prompt.</p></li><li data-preset-tag="p"><p><strong>Image-to-video (</strong><code><strong>i2v</strong></code><strong>):</strong> Provide one or more images using <code>keyframes</code>.</p></li><li data-preset-tag="p"><p><strong>Video continuation (</strong><code><strong>v2v</strong></code><strong>):</strong> Provide an existing clip using <code>start_video</code> to continue its scene.</p></li><li data-preset-tag="p"><p><strong>Draft enhancement (</strong><code><strong>draft_enhance</strong></code><strong>):</strong> Use the <code>draft_cache</code> from a previous draft to generate its full-quality version.</p></li></ul><blockquote><p dir="auto"><strong>Note</strong>: The API returns signed video URLs, so download the generated files to your own storage when you need to retain them.</p></blockquote><p dir="auto"><strong>Learn More:</strong> <a href="https://docs.bfl.ml/flux_3/flux3_overview">FLUX 3 overview</a> | <a href="https://docs.bfl.ml/flux_3/flux3_video">FLUX 3 Video documentation</a> | <a href="https://docs.bfl.ml/api-reference/utility/generate-a-video-with-flux-3">Video API reference</a> | <a href="https://docs.bfl.ml/quick_start/pricing">FLUX 3 pricing</a> | <a href="https://bfl.ai/blog/flux-3-video">Official announcement</a></p><p dir="auto">FLUX 3 Video supports audio generation, frame control, and continuation through one endpoint.</p><p dir="auto">The final API is another option for short videos with dialogue and specific reference-frame controls.</p><h3 dir="auto">9. xAI Grok Imagine API</h3><iframe src="https://www.youtube.com/embed/zpmVtZekVbg?iv_load_policy=3&amp;rel=0&amp;modestbranding=1&amp;playsinline=1&amp;autoplay=0&amp;mute=1" data-thumbnail="Medium Quality" frameborder="0" allow="presentation; fullscreen; accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture"></iframe><p dir="auto">xAI provides video generation through the Grok Imagine API. Its <code>grok-imagine-video-1.5</code> model supports text-to-video, image-to-video, and reference-based generation.</p><p dir="auto">The model can generate clips up to 15 seconds long and supports audio with lip-synced speech. It also accepts reference images and voice inputs, along with controls for the first, last, and intermediate frames.</p><p dir="auto">This is useful when your application needs to define what appears at particular moments. For example, you could guide a generated scene using character images and specify how it should begin or end.</p><p dir="auto"><strong>Best for:</strong> Short videos that need generated dialogue, reference images, or precise frame guidance.</p><p dir="auto"><strong>When to use</strong></p><p dir="auto">Grok Imagine is useful when speech or frame control matters to your application.</p><ul dir="auto"><li data-preset-tag="p"><p>Generate short videos containing dialogue.</p></li><li data-preset-tag="p"><p>Create product and promotional clips.</p></li><li data-preset-tag="p"><p>Use reference images to guide subjects.</p></li><li data-preset-tag="p"><p>Control first, last, and intermediate frames.</p></li><li data-preset-tag="p"><p>Produce videos with configurable duration and resolution.</p></li></ul><p dir="auto"><strong>How to install</strong></p><p dir="auto"><strong>Step 1.</strong> Create an account at <a href="https://console.x.ai/">xAI Console</a> and obtain an API key.</p><p dir="auto"><strong>Step 2.</strong> Install the official Python SDK.</p><pre data-language="Shell"><code>pip install xai-sdk</code></pre><p dir="auto"><strong>Step 3.</strong> Configure your API key.</p><pre data-language="Shell"><code>export XAI_API_KEY="your_api_key"</code></pre><p dir="auto"><strong>Step 4.</strong> Generate a video.</p><pre data-language="Python"><code>import os

import xai_sdk

client = xai_sdk.Client( api_key=os.getenv("XAI_API_KEY") )

response = client.video.generate( model="grok-imagine-video-1.5", prompt=( "A small robot walking through a city " "at sunset. The camera follows its movement." ), duration=5, )

print(response.url)

The SDK submits the generation request, polls for completion, and returns the completed video.

For more control, you can use the documented reference-to-video options to provide images, voice references, or specific frames.

Note: The model supports native 1080p output for text-to-video and image-to-video, while reference-to-video is limited to 720p.

Learn More: xAI video overview | Video-generation documentation | xAI API quickstart

How to choose the right AI video API

The easiest way to narrow your options is to start with the video you want to generate and the controls your application needs.

• If you're working with a specific video model, direct access through Gemini, Luma, Wan, or xAI gives you its documented generation options. Runway also provides video models and production APIs.

• If you want to compare different models, fal and OpenRouter let you work through one integration while selecting a supported model for each request.

• On fal, you can use:

  • Kling for planned shots,

  • Seedance for longer videos with references, and

  • MiniMax H3 for motion and audio.

  • Higgsfield is another option when you need its creative operations.

For AI agents, the decision is a little different.

If an agent needs to generate videos and then work with connected applications, Composio Instant can provide access to eligible paid generation tools without separate provider credentials.

Before choosing, test these five things:

1. Input support: Does the API accept the text, images, videos, or references your application needs?

2. Output limits: Does the model support the required duration, resolution, and audio?

3. Generation cost: What does the actual request cost with your chosen settings?

4. Task handling: Can your application manage polling, failures, and output downloads?

5. Access requirements: Is the model publicly available, in preview, or restricted to approved accounts?

These checks help one narrow down options before spending time integrating multiple providers.

Conclusion

Several ways exist to add AI video generation to an application, and the right API depends on what you're building.

Some developers need direct access to a model and its controls, while others need multiple models or generation as part of an automated workflow.

• For direct generation and editing, Gemini, Runway, Luma, Wan, and Grok Imagine offer different capabilities.

• If you want access to models such as Kling, Seedance, and MiniMax H3 through one integration, fal provides a way to use them without setting up separate provider accounts.

• OpenRouter is useful for applications already using multiple AI models, while Higgsfield adds creative operations.

• And if you're building an AI agent that needs to generate videos alongside other actions, Composio Instant provides access to supported paid tools through an agent-oriented workflow.

Start with one real use case and test two or three suitable APIs. Compare the output, actual cost, generation time, and integration requirements before choosing the one you'll use in production.