OpenRouter for Media APIs: Is There a Generative Media Equivalent?
You've already wired OpenRouter into your LLM pipeline — one API key, model routing across GPT-4o and Claude and Gemini, no per-provider account juggling. Now you're adding AI-generated images and video to the same product. The natural question: is there an OpenRouter equivalent for generative media?
The short answer is yes. GenRelay is a unified generative media API platform — one endpoint, one auth token, access to the leading image and video models available today. This article explains how the pattern maps from the LLM space to the media space, what's different, and how to integrate it.
What Does OpenRouter Do for LLMs — and Why Media Needs the Same Thing?
OpenRouter provides a unified API that routes requests across language models from different providers. The value for developers is concrete: no multi-provider auth management, a single billing account, the ability to switch models with a parameter change.
Generative media has the same fragmentation problem, multiplied. To build a product that generates images and video, you'd traditionally maintain separate integrations for:
- Image: Google's Imagen API (Nano Banana Pro, Nano Banana 2), OpenAI's image API (GPT-image-2), and their separate auth flows and SDKs.
- Video: Google's Veo 3.1 API, Grok Imagine 1.0/1.5, Google Gemini Omni Flash — each with distinct job submission patterns, async polling intervals, and billing units (per-second vs per-generation).
A unified media API eliminates that overhead. You maintain one API key, call one endpoint, and route to any model with a model parameter change.
How GenRelay Maps to the OpenRouter Pattern
GenRelay provides a REST API at https://api.genrelay.ai/v1/ with separate endpoints for images and video, and a consistent auth model across both.
Authentication is identical for every model:
import requests
headers = {"Authorization": "Bearer YOUR_GENRELAY_KEY"}
That same header works whether you're calling Nano Banana Pro, GPT-image-2, or Veo 3.1 — no per-provider credential management.
Image generation uses a single endpoint with a model parameter:
# Nano Banana Pro — 2K resolution
resp = requests.post(
"https://api.genrelay.ai/v1/images/generations",
headers=headers,
json={
"model": "nano-banana-pro",
"prompt": "A minimalist product photo of wireless headphones on white",
"size": "2048x2048",
"n": 1
}
)
image_url = resp.json()["data"][0]["url"]
# Switch to GPT-image-2 with one parameter change
resp = requests.post(
"https://api.genrelay.ai/v1/images/generations",
headers=headers,
json={
"model": "gpt-image-2",
"prompt": "A minimalist product photo of wireless headphones on white",
"size": "1024x1024",
"n": 1
}
)
Video generation uses an async pattern (job submission → poll → retrieve):
# Submit a Veo 3.1 Lite video job
resp = requests.post(
"https://api.genrelay.ai/v1/videos/generations",
headers=headers,
json={
"model": "veo-3-lite",
"prompt": "A drone flying over a coastal city at sunrise",
"duration": 8,
"resolution": "720p"
}
)
job_id = resp.json()["id"]
# Poll for completion
import time
while True:
status = requests.get(
f"https://api.genrelay.ai/v1/videos/generations/{job_id}",
headers=headers
).json()
if status["status"] == "completed":
print(status["output"]["url"])
break
time.sleep(5)
The polling pattern is consistent across Veo 3.1, Grok Imagine, and Omni Flash — you don't need a separate polling loop per model.
What Models Are Available as of August 2026?
GenRelay provides access to the following production models:
| Category | Model | Key capability |
|---|---|---|
| Image | Nano Banana Pro | Up to 4K, instruction-following, Google's top image model |
| Image | Nano Banana 2 | 1K–4K, cost-efficient alternative |
| Image | GPT-image-2 | Native inpainting + editing, OpenAI model |
| Video | Veo 3.1 Lite | t2v / i2v / ref2v, up to 1080p, per-second billing |
| Video | Grok Imagine 1.0 | t2v, cinematic style, per-second billing |
| Video | Grok Imagine 1.5 | i2v (image-to-video), per-second billing |
| Video | Gemini Omni Flash | t2v / i2v, per-generation billing |
See the full model details on the Nano Banana Pro model page and GPT-image-2 model page.
How Pricing Compares Across Models
As of August 2026 (GenRelay published rates):
Image models:
| Model | 1K resolution | 2K resolution | 4K resolution |
|---|---|---|---|
| Nano Banana Pro | $0.030/img | $0.030/img | $0.042/img |
| Nano Banana 2 | $0.020/img | — | $0.036/img |
| GPT-image-2 | $0.014/img | — | — |
Video models (selected):
| Model | 720p | 1080p | Billing unit |
|---|---|---|---|
| Veo 3.1 Lite | $0.060/s | $0.120/s | Per second |
| Grok Imagine 1.0 | $0.010/s | — | Per second |
| Grok Imagine 1.5 | $0.022/s | — | Per second |
| Omni Flash | $0.10/gen | $0.15/gen | Per generation |
For a 5-second 720p clip: Veo 3.1 Lite = $0.30, Grok 1.0 = $0.05, Omni Flash = $0.10. For detailed pricing breakdowns, see Multi-model image API comparison 2026.
Key Differences from the LLM API Pattern
Async vs synchronous responses. LLM APIs stream tokens in seconds. Video generation takes 20–120 seconds and always returns a job ID first. Image generation is synchronous for single images. Your error-handling and timeout logic must account for this.
Billing units differ by modality. LLMs bill per token. Image APIs bill per image (and sometimes per resolution tier). Video APIs bill per second of output or per generation, depending on the model.
Output is a URL, not text. Images and videos are returned as temporary hosted URLs. Download and store them in your own S3 bucket or CDN before the expiry window (typically 24 hours).
No automatic model substitution. OpenRouter can fall back to an equivalent LLM if one provider is down, because language models are largely interchangeable for text tasks. Video models produce fundamentally different output styles and capabilities — you choose the model explicitly, and GenRelay returns a standard error (503 with retry_after) if a model is unavailable. Your application handles fallback logic.
What OpenRouter Does That GenRelay Doesn't
OpenRouter's primary use case is automatic model routing and fallback for LLMs, optimizing for cost or latency transparently. GenRelay doesn't do transparent fallback across media models — a request for a 10-second cinematic video can't be silently rerouted to an image model. What GenRelay provides is consistent auth, billing, and API semantics across providers, so you switch models deliberately rather than automatically.
For more background on the unified API pattern, see What is a unified AI media API.
Getting Started
- Sign up at genrelay.ai — free credits included.
- Generate an API key in the console.
- Replace your existing provider API calls with the GenRelay endpoint, keeping the same
modelparameter. - Test both image and video endpoints in the console playground before wiring up production.
If you're building a product that combines AI image generation and video generation, a unified media API reduces the integration surface from four separate provider accounts to one. That's the same tradeoff OpenRouter makes for LLMs — applied to the generative media stack.
FAQ
Does GenRelay support the same models as Fal or Replicate?
GenRelay focuses on production-grade models from top providers: Google (Veo 3.1, Nano Banana Pro/2), OpenAI (GPT-image-2), and xAI (Grok Imagine 1.0/1.5). The catalog differs from Fal and Replicate, which include more experimental and community-contributed models.
Can I use one API key for both image and video?
Yes. A single GenRelay API key authenticates against all image and video endpoints — no separate credentials per modality or provider.
Is there a free tier?
GenRelay offers free credits on signup. Paid plans start at $14/month for included compute, plus pay-as-you-go for usage beyond the plan.
Do I need to handle different response formats per model?
No. The response schema is normalized — images return data[].url, video jobs return an id for polling and output.url on completion, regardless of the underlying model.
What if I need a model not yet on GenRelay?
The model catalog expands regularly. Check the GenRelay console for the current model list, or contact support to request a specific model.