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Model APIs gives you instant access to state-of-the-art AI models for image, video, audio, and multimodal generation. Every model is already optimized and production-ready, so you can authenticate and start generating immediately. Each model runs on fal’s infrastructure with automatic scaling, queue-based reliability, and pay-per-use billing. You call them the same way whether you use the Python or JavaScript client or raw HTTP. If you need to deploy your own model instead, see Serverless.

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Quick Example

Generate an image in three lines of code. Install the client, set your API key, and call a model. 
import fal_client

result = fal_client.subscribe("fal-ai/nano-banana-2", arguments={
    "prompt": "a futuristic cityscape at sunset"
})
print(result["images"][0]["url"])
The response includes a CDN URL for the generated image, along with metadata like dimensions and seed. Every model follows the same pattern: send inputs as JSON, receive outputs as JSON with media URLs.

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How It Works

Every model on fal is exposed as an HTTP endpoint. You can call it directly, or go through the queue for automatic retries, status tracking, and scaling. There are several calling patterns depending on your use case. Direct (run) sends a synchronous HTTP request to fal.run and returns the result directly. This is the simplest approach for quick scripts and prototyping. Subscribe uses the queue under the hood but handles polling automatically, so it feels synchronous. This is what the Quick Example above uses. Asynchronous (submit) gives you full control over the queue. Submit a request and return immediately, then poll for status or receive results via webhook. This is the recommended approach for production workloads with parallel processing. Streaming delivers output progressively as the model generates it. This is useful for LLMs that produce tokens incrementally, or for showing generation progress in a UI. realtime() uses WebSockets for persistent connections, bypassing the queue entirely for sub-100ms latency. Only available for models with an explicit real-time endpoint.

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What You Can Generate

The model gallery has 1,000+ models spanning several categories. Here are some popular starting points.

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Image Generation and Editing

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Nano Banana 2

Google’s fast image generation and editing model
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Nano Banana Pro

State-of-the-art image generation with realism and typography
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Flux 2 Flex

Enhanced typography and text rendering from BFL
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Recraft V4 Pro

Professional design and marketing visuals
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Nano Banana 2 Edit

Intelligent image editing with Google’s latest model
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FLUX Kontext Pro

Targeted edits and complex scene transformations

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Video Generation

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Veo 3.1

Google DeepMind’s latest video model with sound
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Kling 3.0 Pro

Cinematic image-to-video with fluid motion
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Kling O3

Start and end frame animation with scene guidance
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Sora 2

OpenAI’s video model with audio generation
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LTX-2 19B

Video with audio from images using LTX-2
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Sora 2 Pro

OpenAI’s premium video model with enhanced quality

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Audio and Speech

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Chatterbox TTS

Natural text-to-speech from Resemble AI
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MiniMax Speech-02 HD

High-quality multi-voice text-to-speech
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Dia TTS

Multi-speaker dialogue with voice cloning
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Beatoven Music

Royalty-free instrumental music generation
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Beatoven SFX

Professional sound effect generation
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ElevenLabs Music

High quality, realistic music generation

Explore All Models

Browse 1,000+ models across image, video, audio, 3D, and more
Every model page on fal.ai includes a Playground for testing, full API documentation with input/output schemas, pricing, and ready-to-copy code examples.

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Next Steps

Playground

Test and compare models interactively before integrating

Inference

Learn the different ways to call models

Client Setup

Install and configure the fal client for Python, JavaScript, and more

Examples

Step-by-step tutorials for image, video, and audio generation