Flux 2 vs GPT-Image 1.5
Hyperrealistic renders indistinguishable from photographs — see how these models compare with real AI-generated outputs.
Full comparisonCompare Models (select 4)
For photorealistic work—images that can pass as real camera captures—small differences in skin texture, lens behavior, lighting falloff, and background coherence matter. Flux 2 and GPT-Image 1.5 both target high-fidelity output, but they approach “photo-real” from different strengths: controllability and editing depth vs prompt-true generation.
Below is a practical comparison focused on hyperrealistic results: how reliably each model produces believable people and products, how well it holds up under close inspection, and which workflows (editing, style transfer, LoRA, resolution) help you land consistent, photographic renders.
Which Model Should You Choose?
Short answer: Flux 2 is better for style control & LoRA workflows, while GPT-Image 1.5 is better for accurate prompt adherence. If you are creating photorealistic, start with GPT-Image 1.5 because it costs fewer credits per output and lets you test more directions, then switch to Flux 2 for polished, higher-resolution final assets.
| If you need… | Choose | Why |
|---|---|---|
| Lower-cost exploration and more variants per credit | GPT-Image 1.5 | GPT-Image 1.5 costs 8 credits to start, so you can test more directions for less. |
| Polished, ready-to-ship final assets | Either model | Either model produces stronger final-asset polish for campaign-ready output. |
| Readable text in designs, overlays, and packaging | GPT-Image 1.5 | GPT-Image 1.5 renders labels and typography more cleanly. |
| Editing and reference-driven iteration | Flux 2 | Flux 2 is more flexible for editing from references or existing outputs. |
| Consistent characters and repeated campaign visuals | Flux 2 | Flux 2 holds character and style consistency better across outputs. |
| Photorealistic specifically | Flux 2 | Flux 2 scores higher on realism, which matters most for photorealistic. |
How They Compare, Criterion by Criterion
| Criteria | Flux 2 | GPT-Image 1.5 | Winner |
|---|---|---|---|
| Realism | ●●●●○ | ●●●●○ | Tie |
| Text accuracy | ●●●○○ | ●●●●○ | GPT-Image 1.5 |
| Editing flexibility | ●●●●● | ●●●○○ | Flux 2 |
| Cost efficiency | ●●●●○ | ●●●○○ | Flux 2 |
| Final polish | ●●●●○ | ●●●●○ | Tie |
| Consistency | ●●●●● | ●●●●○ | Flux 2 |
| Best first test | ●●●●○ | ●●●○○ | GPT-Image 1.5 |
How We Compare These Models
Models compared
Flux 2 vs GPT-Image 1.5
Use case
Photorealistic
Flux 2 — best for
style control & LoRA workflows
GPT-Image 1.5 — best for
accurate prompt adherence
Flux 2 — avoid if
Accurate rendered text is your top priority
GPT-Image 1.5 — avoid if
You need the lowest cost or advanced editing flexibility
Credits per image (Flux 2)
22 credits
Credits per image (GPT-Image 1.5)
8 credits
Last updated
June 8, 2026
What the Examples Show
Realism
Both models produce comparably natural results in these examples.
Text accuracy
GPT-Image 1.5 renders any labels, overlays, or typography more cleanly.
Commercial usability
Either output is close to a usable asset with light cleanup.
Recommended next step
Use GPT-Image 1.5 for first-pass variants, then Flux 2 for final polish.
Photorealistic — Side-by-Side Results
Prompt
"Hyperrealistic DSLR-quality photo of a 20s woman with shoulder-length wavy dark brown hair and minimal makeup, wearing an oversized gray hoodie and black leggings, holding her phone up for a mirror selfie while glancing slightly toward the camera with a relaxed half-smile. She’s in a slightly messy bedroom getting ready—open closet behind her, unmade bed, skincare bottles on a dresser—captured in soft natural window light with realistic skin texture, flyaway hairs, and subtle under-eye shadows. Candid “GRWM” vibe like an Instagram story, handheld framing with tiny motion blur and true-to-life indoor lighting."
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
- LoRA support for consistent photoreal subjects, faces, and brand/product looks across a series
- Versatile image-to-image editing for refining realism (lighting tweaks, background swaps, wardrobe/product changes) without restarting
- Up to 4MP output for sharper “camera-like” detail and better crops for ads and social placements
- Style transfer and face-swap support for controlled variations while keeping a realistic base
- Flexible workflow for iterative realism: generate → edit → upscale/crop → re-edit
GPT-Image 1.5 Strengths
- Strong prompt adherence for photoreal briefs with many constraints (wardrobe, setting, lens cues, composition, props)
- High-fidelity text-to-image output that can render detailed scenes with realistic materials and lighting
- Good at producing coherent, story-like frames where multiple elements must match the prompt
- Tiered quality/credit options (low/medium/high) to balance photoreal detail vs cost per image
- Efficient for “one-shot” photoreal generations when you don’t need heavy post-editing
Verdict
If your definition of photorealistic includes repeatability (same person/product across many images) and hands-on refinement (surgical edits to make the render feel truly photographed), Flux 2 is typically the stronger workflow thanks to LoRA support, image editing, and up to 4MP output.
If you prioritize prompt-accurate photoreal generation—especially complex scenes with lots of specified details—and you want flexible spend levels per render, GPT-Image 1.5 is a solid pick. Many teams use it for fast, prompt-true “hero frames,” then rely on an editing-centric model when continuity and micro-fixes become the priority.
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