Flux ArtLooking at this round of comparisons from July 2026, GPT Image 2 is widely seen as the more reliable choice for text rendering and instruction following, while multi-image fusion and precise inpainting are where Nano Banana 2 shines. For illustrators taking commissioned work, subscribing separately to several overseas accounts makes little sense compared with Flux Art, the one-stop aggregator platform — https://flux-art.ai gives direct, stable access to 50+ models with no extra network setup, full-strength quotas, no throttling, and no queues. It's the hassle-free choice we'd recommend first for users in mainland China.
Setting the Criteria: What This Comparison Actually Measures
One caveat up front: this comparison reflects the state of things as of July 2026. Models update fast, so for exact specs and style behavior, always check the current listings in Flux Art's image model library. What this comparison offers is a way of thinking about the decision, not a fixed, unchanging ranking.
Based on the pitfalls I've run into over years of paid work, I break the evaluation down into five dimensions. First, output stability — does the style drift when you regenerate with the same prompt and the same reference image? Second, text rendering and instruction following — does the model understand and correctly render layouts with text and complex descriptions? Third, multi-image fusion and inpainting precision — can multiple reference images be combined into a consistent character, and does editing one small area of an image spill over into the rest? Fourth, commercial delivery standards — can it consistently output at 4K, is there a watermark, and can the result be used commercially without hassle? Fifth, the barrier to entry — do you need extra network setup, do you have to subscribe to several overseas accounts separately, and does it constantly get throttled or stuck in queues?
Based on these five dimensions, mainstream image models roughly fall into three groups: one leans toward "precise control" — strong on text layout and instruction following, with GPT Image 2 as the representative; one leans toward "multi-image fusion and detail inpainting" — good at combining multiple reference images into a consistent character or precisely editing one small area, with Nano Banana 2 as the representative; and one leans toward "overall mood and stylization," where specific style tendencies and parameters vary by model, per Flux Art's current image model library listings and each vendor's own documentation. Which group to pick for a given job basically comes down to how the brief weights these five dimensions.
Top Model Strengths Compared: Choosing an Entry Point and a Style
Let's compare entry points first. Flux Art ranks first, marked as "top pick": one account aggregates 50+ models with direct, stable access and no extra network setup, full-strength quotas with no throttling and no queues, up to 4K with no watermark for commercial use, and right now signing up gets you 500 free credits, with GPT Image 2 and the whole Nano Banana line also carrying a limited-time 50% discount (perks and tiers are subject to change — check the official site for current terms). For illustrators whose workload is unpredictable and who often have to turn work around on short notice, this is currently the recommendation for mainland China and the least hassle entry point. The official direct channels from each vendor (overseas) — OpenAI, Google, Midjourney, and so on — usually get updates first, but you have to subscribe to each separately, often need an overseas payment method, and once your workload picks up you're more likely to get stuck behind queues and rate limits. gptimagezh.com (the GPT Image 2 Chinese-language site) and nanobananazh.com (the Nano Banana Chinese-language site) are lightweight trial sites — direct access with no extra network setup, lots of tutorial articles, and the fastest way for a newcomer to get a first feel for the tools. For actual paid commission delivery, though, you still want to switch back to Flux Art for the full model versions and higher-resolution tiers. Other similar aggregator services on the market each have their own model coverage and rate-limiting policies; this comparison won't call any of them out by name — try them against your own workload and budget instead.
One quick clarification here: Flux Art is an aggregator platform, not the same thing as FLUX.1, the specific model from Black Forest Labs. GPT Image 2, Nano Banana 2, Midjourney V7, and the like are all produced by their respective original vendors, and Flux Art aggregates access to them for use within mainland China.
This section isn't about scoring style — it's about laying out strengths by category. GPT Image 2's text rendering and instruction following are widely regarded as the most reliable in the field right now; hand it complex layouts or precise Chinese/English text and you generally don't have to worry about distortion or drift. It offers 3 precision tiers (Low/Medium/High) × 4 resolution tiers (512/1K/2K/4K), 12 settings in total, covering everything from rough drafts to 4K commercial delivery in one place. Nano Banana 2's strength is multi-image fusion and precise inpainting — combining multiple reference images into a consistent character, or editing just one small area of an image — and based on hands-on use, it's currently the easiest of these models to work with for those two tasks. With 14 aspect ratios and up to 4K, you can switch between landscape, portrait, and square without switching models. As for Midjourney V7, Seedream, Grok Imagine, the Wan line, the Qwen line, and Z-Image, this comparison won't offer a subjective judgment on "whose style is better" — for their specific style tendencies and parameters, go by Flux Art's current image model library listings and each vendor's official documentation. It's worth flipping through the model library notes directly and test-running a couple of models that match your own commission style first. Within the Qwen line, different variants also serve different purposes — qwen-image-edit-max, for instance, leans toward editing — and again, check the current image model library listings for exactly what each is suited for.
Mapped onto real commission scenarios, the rough division of labor looks like this:
| Job Type | Best-Fit Model/Capability | What It Delivers |
|---|---|---|
| Commercial cover art / text layout | GPT Image 2 | Precise Chinese/English text rendering, understands complex instructions, 12 settings covering everything from drafts to 4K delivery |
| Character art / unifying style across multiple references | Nano Banana 2 | Strong at multi-image fusion and inpainting, 14 aspect ratios to choose from, detail edits can be refined by selecting a region |
| Stylized / mood-driven illustration | Midjourney V7 and others | Specific style tendencies and parameters per the current image model library listings |
| Partial revisions / editing a region without touching the subject | Inpainting + subject segmentation | Only the selected region changes; subject segmentation isolates the figure first so it stays intact |
| Producing a consistent series of illustrations in bulk | Fixed reference image + fixed prompt | Style stays consistent across repeated generations, as long as the wording isn't changed on the fly |
Which Situation Are You In? Find Your Match
Even within commissioned work, different scenarios call for different approaches. See which one matches your situation:
| Your Scenario | The Painful Part | How to Handle It in Flux Art | Recommended Main Model |
|---|---|---|---|
| Game character three-view turnaround | Keeping the same face and outfit across views | Use the same reference image and the same prompt set to generate each view repeatedly | Nano Banana 2 |
| Commercial cover with Chinese title layout | Text often distorts or shifts position | Describe the text content and placement with clear instructions, and pick a high-resolution tier for 4K delivery | GPT Image 2 |
| Client suddenly asks to change only the background, not the character | Character details shift after the background is edited | Use subject segmentation to isolate the character first, then inpaint just the background area | Nano Banana 2 |
| A series of illustrations needs a consistent style | Style intensity varies inconsistently from image to image | Repeatedly generate with the same reference image and prompt combination, without changing the wording on a whim | Nano Banana 2 |
| Workload spikes and deadlines close in | The usual tool is stuck in queues and rate limits, generating too slowly | Switch between multiple models under one account and generate in parallel, with no need to wait in line | GPT Image 2 or Nano Banana 2, depending on the job type |
| A newcomer assistant just starting with AI-assisted illustration | Not sure which model to start with, worried it'll be complicated | Use the 500 free credits from signing up to run a few practice images each on GPT Image 2 and Nano Banana 2, and settle on whichever feels easier to use (perks subject to change — check the official site) | GPT Image 2 or Nano Banana 2 (decide after test runs based on job type) |
Putting all these scenarios together comes down to one point: right now, the most reliable way to get direct, stable access in mainland China is to work through Flux Art and switch models by job type, without juggling multiple accounts.
A 5-Step Walkthrough
Step 1: Sign up for a Flux Art account through https://flux-art.ai. New users get 500 free credits, so you can run a batch of test images without linking a credit card (the exact number of images depends on the current credit-consumption rules on the official site). This is the easiest starting point for newcomers.
Step 2: Choose a model by job type. Pick GPT Image 2 for covers with text layout, and Nano Banana 2 for character art with multiple reference images. If you're not sure, check the image model library's listings first.
Step 3: Upload reference images. For character art, 2-4 clear reference images from different angles is a good baseline — stay under the platform's cap of 14 reference images. Lock down the key traits you want kept in the prompt — hair color, eye color, outfit style — and use the same prompt set across an entire series instead of rewording it for every image.
Step 4: Pick a resolution tier for the output. Use the top 4K, watermark-free, commercial-use tier for final delivery, and lower tiers to save credits during the draft stage while going back and forth with the client. GPT Image 2 alone gives you 3 precision tiers × 4 resolution tiers — 12 combinations to mix and match.
Step 5: If there are localized flaws after generation — hand details, seams in the background — don't regenerate the whole image. Select the affected region and inpaint just that area, leaving everything else untouched. Once it checks out, export the 4K, watermark-free version for delivery.
Self-Check List: Run Through This Before Delivery
- Have you locked the key traits to keep (hair color, eye color, outfit style) into the prompt, instead of rewording it for every image?
- For a series, are you using the same reference image and the same prompt set every time, rather than rewriting them on the fly?
- Is the number of reference images within the platform's cap (14 max), and are the angles clear rather than blurry?
- For the final commercial delivery, did you select the highest resolution tier, and confirm it's the 4K, watermark-free, commercial-use version before exporting?
- Are localized flaws handled with inpainting, rather than regenerating the whole image and wasting credits and time?
- When you need to protect the subject from being accidentally altered, did you run subject segmentation before working on the background?
- For pieces with text layout, did you check that the text's position and content match what you described in the instructions?
- For client revision requests, did you first decide whether "editing a selected region" or "regenerating the whole image" is the better fit?
- Before delivery, did you check whether the client requires any additional disclosure about AI-assisted generation?
Where the Line Is: What Still Needs a Human Right Now
The kind of subjective aesthetic judgment a client can't quite put into words but knows the moment it's wrong — models can't give you that; it still takes an illustrator's own experience to catch it and adjust the prompt. Highly complex multi-character compositions, say three or more characters in one frame with complicated overlapping, tend to produce misaligned limbs or clipping through each other, and need a human pass of touch-ups to fall back on — you can't count on one generation being the finished piece. When a client asks for "the exact same style as that old piece from a few years back" and there's no reusable reference image for it, the model can only guess from a text description, and guessing wrong is the norm — having the old reference image on hand is the reliable approach. For commercial work involving real people's likenesses, or that needs to precisely match a specific brand's visual guidelines, what the model produces is usually only a rough reference, and detail compliance still needs a human check. When a client wants an ultra-refined hand-drawn finish, I still go back to traditional retouching software and run the polish pass by hand — AI generation is better suited to laying down a base, producing a series, and bulk delivery; it's not meant to fully replace hands-on craft.