Flux 2 vs GPT-Image 1.5
Buildings, facades, and architectural visualization — see how these models compare with real AI-generated outputs.
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For architecture & exterior work, the difference between a “nice image” and a usable visualization often comes down to facade geometry, material realism, consistent design language, and how reliably the model follows constraints like window rhythm, massing, and camera angle.
Flux 2 and GPT-Image 1.5 both generate strong building exteriors, but they excel in different parts of the workflow. Flux 2 leans into iterative design and art-direction with editing tools, style transfer, and LoRA customization (plus up to 4MP output). GPT-Image 1.5 prioritizes prompt adherence and high-fidelity renders—useful when you need the model to respect a detailed architectural brief.
Architecture & Exterior — Side-by-Side Results
Prompt
"A candid phone-camera wide selfie of a 20s woman with shoulder-length wavy brown hair in a casual oversized hoodie, bike shorts, and white sneakers, standing on a sidewalk and glancing near the lens while holding an iced coffee. Behind her is a striking modern glass-and-concrete building exterior shot at a dramatic low angle with strong leading lines and reflections, golden hour sunlight and a warm sky, like an Instagram story “quick coffee run” moment. Natural street lighting, slight motion blur from walking, authentic unposed vibe."
Feature Comparison
| Feature | Flux 2 | GPT-Image 1.5 |
|---|---|---|
| Provider | Black Forest Labs | OpenAI |
| Subcategories | text-to-image, image-to-image | text-to-image |
| 1080p / 2k Mode | Yes | Yes |
| 4k Mode | Yes | No |
| NSFW Rating | Low | Strict |
| Aspect Ratio | 1:1, 16:9, 9:16, 3:4, 4:3 | 1:1, 16:9, 9:16, 3:4, 4:3 |
| Model Variant | Standard, Klein 9B | — |
| Starting Price | 22 credits | 8 credits |
Flux 2 Strengths
- Iterative exterior design workflows: image-to-image editing makes it easier to refine massing, facade patterns, and streetscape context without restarting
- Customization for architectural “house styles” via LoRA support (e.g., consistent modernist glazing ratios, brick-and-limestone palettes, or a specific visualization look)
- Up to 4MP output for sharper facade details (mullions, cladding seams, balcony rails) and cleaner presentation boards
- Style transfer support for quickly exploring exterior directions (minimalist, neo-classical, brutalist, coastal, etc.) while keeping the same base composition
- Versatile editing capabilities that suit concept iterations, value engineering visuals, and before/after exterior renovations
GPT-Image 1.5 Strengths
- Strong prompt adherence for architectural constraints (camera height, lens feel, facade rhythm, material callouts, and site context)
- High-fidelity exterior renders with convincing lighting, reflections on glazing, and more cohesive scene detail
- Handles detailed scenes well (streetscape activity, landscaping, surrounding buildings) while keeping the primary building readable
- Flexible quality tiers (low/medium/high) that let you balance speed, cost, and final render polish for exterior deliverables
Verdict
Choose Flux 2 if your architecture workflow depends on iterative editing, consistent style systems across a project, or you want to develop a repeatable “studio look” using LoRA. It’s particularly effective for facade exploration and controlled variations from a base concept, with the added benefit of up to 4MP output for presentation-ready detail.
Choose GPT-Image 1.5 if your priority is faithful execution of a detailed exterior brief from text prompts—especially when you need the model to respect specific constraints and deliver high-fidelity results quickly. Its tiered pricing also makes it straightforward to draft in low/medium and finish in high when the exterior render needs to land.
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