Run a model shoot-out for your studio

Run a model shoot-out on one brief. Change only the model: the same prompt, word for word, and the same reference for every run. Never rewrite the prompt for a model.

Reference: the studio image of the car, passed as referenceImages, an array with its asset ID.

Prompt, word for word:
The exact car from the reference image, with its body shape and proportions, liquid satin champagne paint, one continuous thin LED light bar across the full width of the nose, the spaced AURELLE wordmark in chrome capitals centered under the light bar, wide dark lower intake with fine horizontal slats, dark twisted multi-spoke turbine wheels with gold brake calipers, and black glass roof unchanged, parked on a gravel overlook on a high mountain pass at sunrise. Snow-capped peaks and a winding road behind, low warm sun from the left raking along the side of the car, cold blue shadows, thin mist in the valley below. Three-quarter front view at headlight height, the light bar lit and the AURELLE wordmark facing the camera and sharp. Soft contact shadow under the tires on the gravel. 3:2 landscape. No other cars, no people, no other text or logos. Style: automotive launch photography, controlled reflections, perfect panel gaps, no other brands.

Candidates and settings, one image each, at each model's highest quality:
- GPT Image 2.5 Sunburst (model_openai-gpt-image-2-5-sunburst): width 1536, height 1024, quality high
- GPT Image 2.5 Flare (model_openai-gpt-image-2-5-flare): width 1536, height 1024, quality high
- Nano Banana 2.1 (model_google-gemini-nano-banana-2-1): aspect ratio 3:2, resolution 2K
- Seedream 5.0 Pro (model_bytedance-seedream-5-0-pro): 2048 x 1360

Read each model's parameters with model_schema_get first; where a model offers only aspect presets, pick 3:2. Quote the cost with a dry run first and wait for my go.
Launch the four with wait: false and collect them with one jobs_wait. Report each job's cost, wait and output size.

Download the four results, put them in one grid at the same size, and crop the front of each (light bar, wordmark, intake) at the same scale into a second grid. Write a scorecard with one column per model and these rows: wordmark spelled correctly, light bar, wheels and calipers, paint vs reference, light and mood vs brief, artifacts, cost, wait. Fill cost and wait from the jobs and leave the visual rows for me.

Run GPT Image 2.5 Sunburst and GPT Image 2.5 Flare once more each, with exactly the same prompt, reference and settings. Show both new takes next to their first takes.

Create a collection called "Model test pack: product scenes". Add the studio reference and every take to it, and put the test prompt and the settings of each model in its description. Report every asset ID with its model, the cost and wait of each job, and the total cost spent.

The exact car from the reference image, with its body shape and proportions, liquid satin champagne paint, one continuous thin LED light bar across the full width of the nose, the spaced AURELLE wordmark in chrome capitals centered under the light bar, wide dark lower intake with fine horizontal slats, dark twisted multi-spoke turbine wheels with gold brake calipers, and black glass roof unchanged, parked on a gravel overlook on a high mountain pass at sunrise. Snow-capped peaks and a winding road behind, low warm sun from the left raking along the side of the car, cold blue shadows, thin mist in the valley below. Three-quarter front view at headlight height, the light bar lit and the AURELLE wordmark facing the camera and sharp. Soft contact shadow under the tires on the gravel. 3:2 landscape. No other cars, no people, no other text or logos. Style: automotive launch photography, controlled reflections, perfect panel gaps, no other brands.