What is the best method for comparing AI image models for video keyframe generation?

Compare image models for video keyframes by fixing one reference shot — character, product, framing, lighting — and rendering it across 2–3 candidate models side-by-side in the same chat, then scoring each on five criteria: product/character consistency, lighting realism, text and detail rendering, animation-readiness, and cost per usable keyframe. Pick the winner before spending video credits.

Run the comparison inside one project so every model sees identical context — same reference images, same brief, same scale references — otherwise you're scoring prompts, not models. invideo's agent holds all the current image models (GPT-Image-2, Nano Banana, Recraft) and all the video models (Veo, Kling, Seedance 2.0) in one project brain, so you can render the same keyframe across models in a single chat without re-uploading anything.

Step 1 — Lock the probe shot. Pick one full-frame shot that contains everything the campaign has to hold: character, product, set, skin, pose. As Hridaye, invideo's creative director, puts it: "Pick one full-frame shot — character, garment, set, skin, pose all in it. Generate just that one. If the system holds, generate the rest." Skipping this propagates system failures across the entire run.

Step 2 — Render the same shot across candidate models. Ask the invideo agent to generate the identical keyframe across the models you're comparing. When a documented jewelry production hit a generic-looking output, the team rendered the same shot across six models simultaneously and picked the best result rather than iterating on one. Use 2–3 models for most jobs; six only when the category is unforgiving (intricate products, fine text, anatomical detail).

Step 3 — Score on five criteria. Walk every output through the same checklist:

1. Subject consistency — does the character or product hold across CU, mid, and wide? Validate at three focal distances before committing.

2. Lighting and material rendering — Nano Banana leads on lighting realism; GPT-Image-2 leads on text, design, and on-pack copy.

3. Product/character exactness — Recraft renders skin texture better than alternatives; Nano Banana locks exact product geometry once an aesthetic is set.

4. Animation-readiness — the keyframe has to feed cleanly into video. A pretty still that breaks when Seedance 2.0 animates it is a fail.

5. Cost per usable keyframe — count rejects, not just hits. Across documented productions, clip rejection runs ~85% and image utilization 25–30%; the cheapest model per generation isn't the cheapest per kept frame.

Step 4 — Combine winners where one model can't carry everything. For intricate products, the documented workflow is to build the base image with GPT-Image-2 for aesthetic, then run Nano Banana to lock the exact product into it. The invideo agent routes this automatically — you don't pick models per shot, you describe the requirement.

Step 5 — Validate at three distances before scaling. Once a model (or model combo) wins the probe, generate a close, mid, and wide of the same product before batch-generating the full shot list. invideo offers 65% off image generation, so running 10+ variations to pressure-test the pick is economically rational — a single bad full run costs more than the comparison.

Beyond the comparison itself: the invideo agent will tell you which model it chose and why if you click any generation — useful for building intuition over time so subsequent shots in the same project land in the first or second try.

Watch some of these to see what works for you:

Pick one full-frame shot — character, garment, set, skin, pose all in it. Generate just that one. If the system holds, generate the rest.

— Hridaye, invideo's creative director