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Generative video has moved from demo reels into working pipelines: marketing teams producing ad variants, product teams prototyping explainer clips, agencies drafting previs for clients. The hard part is no longer finding a model that can make a nice clip. It is deciding, with evidence, which model and which configuration your team should standardise on, and what that choice will cost at scale.
This piece lays out a lightweight benchmarking method you can run in a week. We use ByteDance's Seedance 2.0 as the worked example because it exposes several of the variables that matter in an evaluation: quality tiers, resolution steps, generated audio and multi-shot output inside a single clip.
Most informal model comparisons fail because they change several things at once. Fix the following up front and record them for every generation:
Tier or model variant. Many video models now ship in speed and quality tiers. Seedance 2.0 is offered as Mini, Fast and High.
Resolution. Cost usually scales with pixels. On the setup we tested, Mini and Fast render at 480p or 720p, while High adds 1080p and 4K.
Duration. Seedance 2.0 accepts 5, 10 or 15 second clips.
Aspect ratio. 16:9, 9:16, 1:1, 4:3, 3:4 and 21:9 are supported, so test the ratios you actually ship.
Input mode. Text to video, image to video with a start frame and optional end frame, or a reference mode that takes multiple images, clips and audio.
A benchmark is only as good as its test set. Pick eight to twelve prompts that represent real jobs, not showcase shots. A sensible starting mix:
A product macro shot, such as a liquid pour with rising bubbles and a light sweep.
A talking-head close-up with one line of dialogue.
A tracking shot with physics: water, cloth or hair in motion.
A vertical 9:16 performance clip for social.
A multi-beat scene that needs cuts inside one clip.
A stylised animation, for instance stop-motion claymation.
Write each prompt in the same structure so differences come from the model, not your phrasing. A shot-note order works well: shot size and camera move, subject, one action, light, lens, then sound. For example:
[0-3s] Wide handheld shot of a night market stall, a cook tosses noodles in a flaming wok. [3-6s] Close-up, noodles drop into a white bowl, steam curling. [6-10s] Medium shot, the cook slides the bowl toward camera and grins. Warm tungsten light, 35mm lens. Sound of sizzling oil and crowd chatter.
The timestamped beats test whether a model can hold character continuity across internal cuts, one of the capabilities that separates current video models from earlier single-shot systems.
Use a simple 1 to 5 rubric, scored blind by at least two reviewers where possible:
Prompt adherence: did the requested action, framing and lens actually happen?
Temporal consistency: do faces, products and props stay stable across frames and cuts?
Physical plausibility: water, fabric, fire and fast motion.
Audio fit: Seedance 2.0 generates a soundtrack with the picture, so score whether the sound matches the action and whether an instruction like "sound effects only, no music" is respected.
Usability: could this clip ship as is, or after a light edit?
List prices per clip mislead because not every clip is usable. Track the acceptance rate for each configuration and divide. Tiered pricing makes this especially worth doing. In the credit-based setup we used for testing Seedance 2.0, Mini costs 1 credit per second at 480p and 2 at 720p, Fast costs 2 and 4, and High runs 3, 6, 11.5 and 23 credits per second from 480p up to 4K. A 5 second High clip at 720p came to 30 credits, while the same length on Mini at 720p was 10.
The useful metric is simple: credits spent across all attempts, divided by the seconds of footage you actually accepted. A cheap tier with a poor acceptance rate can end up costing more than a premium tier that lands first time.
The practical pattern that tends to emerge is to iterate on the cheapest tier until the prompt is right, then re-render only approved prompts on the tier your delivery needs. In our runs, motion, product and animation prompts were often acceptable on Mini at 720p, while faces and dialogue benefited from High. Your mix will differ, which is the point of measuring.
For production use, the API surface matters as much as quality. Check whether tiers map to distinct model identifiers (here, seedance-2-mini, seedance-2-high and seedance-2-reference), how jobs are queued and polled, how long generated assets are retained, and whether reference inputs fit your asset pipeline. Seedance 2.0 Reference accepts up to nine images, three video clips and three audio clips per request, with at least one image or video. That helps with brand consistency but adds asset-management work.
Summarise the benchmark on one page: the prompt suite, rubric averages per configuration, acceptance rate, cost per usable second and a recommended default tier for each job type. Re-run the suite whenever a model version changes. Video models are updating quickly, and a benchmark you can repeat in a day is worth more than a one-off comparison nobody can reproduce.
