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Video understanding demo
Qwen3.6-35B-A3B on ZeroGPU · vs Amazon Nova 2 Lite
Raw footage in. Highlight reel out.
Pick a clip and a sampling rate. Both models get the same frames, and you get a playable shot list, the latency, the token usage, and the cost per video-hour at your settings. The video file stays in your browser; only the sampled frames are sent.
Copy benchmark prompt
View llms.txt
Paste it into Claude Code, Cursor or ChatGPT to run this benchmark on your own clips. All it needs is a ZeroGPU API key.
01 Footage
Drop a clip here
or choose a file · MP4 or MOV
or
Use the sample clip · 27 s whitewater POV
02 Sampling
0.5 fps
1 fps
2 fps
384 px
512 px
768 px
Recommended 1 fps · 512 px: the best balance of detail and tokens for a first test. Use it
Choose a clip to see frame count and tokens.
Qwen3.6 on ZeroGPU · — tok/frame— per video-hour, input · $0.20 / $1.50 per 1M tokens
Nova 2 Lite · ≈240 tok/frame— per video-hour, input · $0.30 / $2.50 per 1M tokens
Run analysis
Choose a clip to start.
Unlocked for · change
Your original clip plays here. Each model’s highlight reel gets its own player below.
The whole integration
OpenAI-compatible: keep your client, change the base URL and the model name.
Python
JavaScript
cURL
List prices per 1M tokens (input / output): Qwen3.6-35B-A3B on ZeroGPU $0.20 / $1.50, volume pricing available. Amazon Nova 2 Lite $0.30 / $2.50 (Amazon Bedrock on-demand, us-east-1). The pre-run Nova estimate uses ~240 tokens per frame, measured through Bedrock (AWS’s own table says ~288); every number in the results is measured from the API's usage fields. Per-video-hour figures extrapolate a run’s measured tokens to an hour of footage sampled the same way.
Sample clip: “Rafting the Klamath River: Bermuda Triangle (Class III, Mile 5.6)”, BLM Oregon & Washington, public domain, via Wikimedia Commons.
ZeroGPU · The compute efficiency layer for AI · zerogpu.ai · Model docs