AI Video API Latency Guide — Generation Time by Model and Resolution
You submitted a video generation job and now you're staring at your polling loop wondering: should the timeout be 2 minutes or 10? Will a 1080p clip take twice as long as 720p? What poll interval avoids hammering the API while still catching the result promptly?
These questions don't have universal answers — they depend on which model you're calling and what resolution you're requesting. This guide breaks down generation time behavior for each video model available on GenRelay, so you can configure your timeouts and polling intervals with data rather than guesswork.
Why does video generation latency vary so much by model?
All four GenRelay video models use asynchronous job submission — you POST a request, receive a job ID, then poll until the job completes. The time between submission and completion varies by model architecture, resolution, and clip duration.
| Model | Billing model | Resolution options | Architecture |
|---|---|---|---|
| Veo 3.1 Lite | Per-second of output | 720p / 1080p / 4K | Diffusion-based, high fidelity |
| Grok Imagine 1.0 | Per-second of output | 720p | Autoregressive, text-to-video |
| Grok Imagine 1.5 | Per-second of output | 720p / 1080p | Autoregressive, image-to-video |
| Gemini Omni Flash | Per-generation (flat) | 720p / 1080p | Throughput-optimized |
Diffusion-based models like Veo 3.1 Lite tend to produce higher visual fidelity but require more computation per frame. Per-generation billing models like Omni Flash are typically optimized for faster throughput — generating quickly is in the provider's interest when price is fixed regardless of time taken.
What poll interval should I use for each model?
Setting your poll interval too short wastes API calls and can trigger rate limits. Too long, and your users wait unnecessarily for results that are already ready.
Recommended polling configuration by model:
| Model | First poll delay | Poll interval | Suggested timeout |
|---|---|---|---|
| Veo 3.1 Lite 720p | 60 s | 30 s | 8 min |
| Veo 3.1 Lite 1080p | 90 s | 30 s | 12 min |
| Grok Imagine 1.0 | 30 s | 20 s | 5 min |
| Grok Imagine 1.5 | 45 s | 20 s | 6 min |
| Omni Flash 720p | 20 s | 15 s | 3 min |
| Omni Flash 1080p | 30 s | 15 s | 4 min |
These are conservative production intervals that err on the side of catching the result within one extra poll cycle rather than minimizing total poll count. Adjust based on your own observed P95 latency for your typical prompt complexity and clip duration.
How do I implement per-model polling with timeout handling?
A clean implementation stores poll configuration per model and raises on timeout rather than silently returning None:
import time, os, requests
POLL_CONFIG = {
"veo-3-lite": {"first_delay": 60, "interval": 30, "max_attempts": 16},
"grok-imagine-1": {"first_delay": 30, "interval": 20, "max_attempts": 15},
"grok-imagine-1-5": {"first_delay": 45, "interval": 20, "max_attempts": 18},
"omni-flash": {"first_delay": 20, "interval": 15, "max_attempts": 12},
}
def poll_video_job(job_id: str, model: str) -> dict:
cfg = POLL_CONFIG.get(model, {"first_delay": 60, "interval": 30, "max_attempts": 20})
headers = {"Authorization": f"Bearer {os.environ['GENRELAY_API_KEY']}"}
url = f"https://genrelay.ai/v1/videos/generations/{job_id}"
time.sleep(cfg["first_delay"])
for _ in range(cfg["max_attempts"]):
r = requests.get(url, headers=headers)
r.raise_for_status()
data = r.json()
status = data.get("status")
if status == "succeeded":
return data
if status == "failed":
raise RuntimeError(f"Job {job_id} failed: {data.get('error')}")
time.sleep(cfg["interval"])
raise TimeoutError(f"Job {job_id} ({model}) did not complete within configured timeout")
Submit and poll in one call:
def generate_video(prompt: str, model: str, resolution: str = "720p", duration: int = 5) -> str:
headers = {
"Authorization": f"Bearer {os.environ['GENRELAY_API_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": model,
"prompt": prompt,
"resolution": resolution,
"duration": duration,
}
r = requests.post(
"https://genrelay.ai/v1/videos/generations",
headers=headers,
json=payload,
)
r.raise_for_status()
job_id = r.json()["id"]
result = poll_video_job(job_id, model)
return result["output"]["url"]
# Example — Grok 1.0 for a quick 720p clip
url = generate_video(
prompt="A timelapse of clouds over mountain peaks, cinematic wide shot",
model="grok-imagine-1",
resolution="720p",
duration=5,
)
print("Video ready:", url)
Critical: store job_id persistently before you start polling. A timeout in your client code doesn't cancel the server-side job — the generation continues and the result will be available. Losing the job ID means losing access to the completed output.
For asyncio-based polling that handles multiple concurrent jobs without blocking, see Async video generation API — polling guide.
How does resolution affect generation time and cost?
Resolution increases both cost and generation time. For per-second billing models, it also changes the price per second of output directly.
Veo 3.1 Lite pricing by resolution:
| Resolution | Price per second of output |
|---|---|
| 720p | $0.060 / s |
| 1080p | $0.120 / s |
| 4K | $0.180 / s |
A 5-second 1080p clip costs $0.60 vs. $0.30 for 720p — double the cost, with longer generation time. For prototyping, preview generation, or user-facing draft outputs, generate at 720p first. Upgrade to 1080p only for final delivery when the content has been approved.
Omni Flash uses per-generation flat pricing:
| Resolution | Price per generation |
|---|---|
| 720p | $0.10 |
| 1080p | $0.15 |
Omni Flash's flat rate means you're not cost-penalized for longer clips within the same generation call. If speed is the primary constraint and cinematic quality is secondary, Omni Flash at 720p gives the fastest turnaround at the lowest cost per job.
How does cost compare across models for the same clip?
Here's what a 5-second clip costs across every model at each available resolution:
| Model | Resolution | Cost per clip |
|---|---|---|
| Grok Imagine 1.0 | 720p | $0.050 (5s × $0.010/s) |
| GPT Omni Flash | 720p | $0.10 (flat) |
| Grok Imagine 1.5 | 720p | $0.11 (5s × $0.022/s) |
| Grok Imagine 1.5 | 1080p | ~$0.11 (same per-second rate) |
| Omni Flash | 1080p | $0.15 (flat) |
| Veo 3.1 Lite | 720p | $0.30 (5s × $0.060/s) |
| Veo 3.1 Lite | 1080p | $0.60 (5s × $0.120/s) |
| Veo 3.1 Lite | 4K | $0.90 (5s × $0.180/s) |
For cost-sensitive high-volume pipelines, Grok 1.0 at $0.010/s is the most affordable per-second option. For quality-first use cases where visual fidelity justifies the cost, Veo 3.1 Lite at 1080p is the reference tier.
For a full model-by-model quality and use-case comparison, see Veo 3.1 vs Omni Flash — which video API to use.
What's the general speed ordering across models?
As of September 2026, approximate generation speed ordering from fastest to slowest:
Fastest → slowest: Omni Flash > Grok 1.0 > Grok 1.5 > Veo 3.1 Lite 720p > Veo 3.1 Lite 1080p > Veo 3.1 Lite 4K
This ordering is influenced by your specific prompt complexity and current platform load. A complex Grok job can take longer than a simple Veo job. Use the poll configuration table above as a baseline and instrument your own jobs to establish actual P95 latencies for your workload.
FAQ
What happens if my polling loop times out — does that cancel the job?
No. A timeout in your client code doesn't cancel the server-side generation. The job continues running and the result will be available at the same job ID URL when it completes. Always persist the job ID before you start polling — in a database, Redis, or even a local file for small scripts.
Do concurrent jobs from the same API key affect each other's latency?
Under sustained load, yes — if you're running many concurrent jobs, queue depth at the provider can increase per-job latency. GenRelay enforces per-key concurrency limits. For the exact limits and how to handle 429 responses, see AI video API rate limits and quotas.
Does clip duration affect latency linearly?
Not always. For diffusion-based models, a 10-second clip doesn't take exactly 2× as long as a 5-second clip — there's per-job overhead for model loading and scheduling. Autoregressive models like Grok tend to scale more linearly with duration. In practice, treat the timeout values in the table above as per-job ceilings regardless of duration, and refine them with observed data from your workload.
What job status values does the GenRelay API return?
Jobs transition through: pending → processing → succeeded or failed. Poll until you see succeeded or failed. Any other status value means the job is still in progress — continue polling.
Is there a way to avoid polling entirely?
Yes — GenRelay supports webhooks. Include a webhook_url in your generation request, and the API will POST the completed result to your endpoint when the job finishes. Webhooks eliminate polling overhead entirely for production systems processing many concurrent jobs.
Summary
Matching your polling configuration to the model you're calling prevents both premature timeouts and wasted API calls. As of September 2026:
- Omni Flash: lowest latency, flat per-generation pricing — use when turnaround speed matters most
- Grok 1.0: lowest cost per second, moderate latency — use for high-volume 720p text-to-video
- Grok 1.5: image-to-video capable, moderate latency — use for i2v workflows at a competitive cost
- Veo 3.1 Lite: highest visual fidelity, longer generation time — use when output quality is the priority
Start with GenRelay's free credits to benchmark actual generation times for your specific prompts and durations before finalizing timeout values in your production config.