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Optimize Kling AI for speed, quality, and cost efficiency.
Passed 20 of 24 test cases with the skill, and 10 of 24 without it.
and 12 more in the full report
10 fixed10 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
We need a mid-tier Kling AI generation configuration that provides significantly higher visual quality than basic drafts without incurring the 35-credit cost of professional mode. Which specific model name and mode string fits this production preview tier, and how many credits does it cost for 5 seconds?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies the model name kling-v2-master | Missed | Passed |
| Specifies standard mode at a cost of 10 credits | Passed | Passed |
The instructions your model reads when the skill loads.
Optimize video generation for your use case by choosing the right model, mode, and parameters. Covers benchmarking, speed vs. quality trade-offs, connection pooling, and caching strategies.
| Config | ~Gen Time | Quality | Credits (5s) | Best For |
|---|---|---|---|---|
| v2.5-turbo + standard | 30-60s | Good | 10 | Drafts, iteration |
| v2-master + standard | 60-90s | High | 10 | Production previews |
| v2.6 + standard | 60-120s | Highest | 10 | Quality-sensitive |
| v2.6 + professional | 120-300s | Highest+ | 35 | Final output |
| v2.6 + prof + audio | 180-400s | Highest+ | 200 | Full production |
import time, requests, json
def benchmark_model(prompt: str, model: str, mode: str = "standard",
runs: int = 3) -> dict:
"""Benchmark generation time for a model/mode combination."""
times = []
for i in range(runs):
start = time.monotonic()
# Submit
r = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
"model_name": model, "prompt": prompt, "duration": "5", "mode": mode,
}).json()
task_id = r["data"]["task_id"]
# Poll
while True:
time.sleep(10)
result = requests.get(
f"{BASE}/videos/text2video/{task_id}", headers=get_headers()
test cases passed, vs 10 / 24 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.