AI Brawl: the Generative Model Showdown

Four engines, one prompt, slightly suspiciously honest commentary

Welcome to the colosseum. Today I’m pitting four image-generation heavyweights against one another: Stable Diffusion, DALL-E, BNX AI, and Google’s Nano Banana (the flashy image/editing arm of Gemini). I wrote this as a first-person lab notebook - a little messy, a little snarky, and committed to testing one single prompt across services so you can see apples vs. cyber-oranges. I ran (and will run) BNX AI and DALL-E myself; I’ll give you the same prompt so you can try Stable Diffusion and Nano Banana at home and judge with your own eyeballs.

Why this matters: BNX AI quietly launched on Product Hunt on October 26, 2025 and immediately advertised a free plan with unlimited downloads and an emphasis on high-quality text-to-image output. That launch - and the claims that BNX «outperforms» even headliners like Nano Banana - deserves a polite, pixel-strewn investigation.

A few quick context points before we brawl:

1. BNX AI advertises itself as a free, text-to-image first generator with a generous free tier and unlimited downloads for testers. The Product Hunt listing and BNX site highlight the «free» aspect and the BNX AI 1.0 engine.
2. BNX’s Terms of Service say: in most cases you own the assets you create, but commercial use and ownership rules change for very large businesses (>$1M revenue) and the service grants BNX a broad licence to use prompts and assets for improvement unless you opt into paid «Stealth Mode» That’s important for anyone planning to build products with generated art.
3. Google’s Nano Banana (part of Gemini 2.5 Flash image capabilities) is positioned as an image-generation and editing powerhouse - especially strong at image-to-image edits and preserving subject consistency across transforms. In short: Nano Banana is designed both to generate and to edit uploaded photos.
4. DALL-E (OpenAI) and Stable Diffusion (community/Stable AI ecosystem) are mature, widely integrated baselines with long histories in text-image work, inpainting, outpainting, and research forks such as SDXL. They remain valuable reference points.

My test prompt (use this exact prompt for fairness)

Prompt: A cinematic, hyper-detailed poster of a futuristic robot barista handing a steaming ceramic cup of coffee to a human customer - extreme close-up on both sets of hands and fingers, showing mechanical articulation and soft, realistic human skin. The robot has a small logo on its chest that reads «BrewNet» in clean sans-serif, warm cinematic rim lighting, shallow depth of field, 8k detail, photorealistic, subtle film grain. Emphasize realistic fingers and correct anatomy, no extra or missing digits, no floating limbs. Include a faint cityscape bokeh in the background.

Why this prompt? It stresses three things many models historically stumble on:

1. Hands and fingers - a known pain point for generative models.
2. Rendered legible text/logo - tests text-in-image fidelity and logo generation.
3. Photo-like detail + artistic lighting - a general quality stress test.

Make no bones: some platforms treat logo text rendering differently (and some platforms have policy filters for logos/trademarks). Use the prompt as a fair stress-check; tweak only the style tokens if you want different aesthetics.

What I tested and how I tested it

I ran this prompt on BNX AI (via bnx.me) and DALL-E (OpenAI). For fairness I left image-size and iterations at site defaults and asked for the highest-quality result each service would give on a free/user tier. I suggest you paste the same prompt into Stable Diffusion (SDXL) and Nano Banana/Gemini and compare results - ideally blind, side-by-side, and paying particular attention to hands, logo/text legibility, and any weird artifacts.

Notes about BNX’s workflow: BNX’s public interface is text-driven (prompt > image); I could not find an «upload»/image-to-image field in their main generator UI, which indicates BNX currently centers on text-to-image only and lacks an image-upload editing pipeline in the public interface. That matters if you want to edit or refine existing photographs.

Also: BNX’s Terms explicitly state most generated assets are owned by creators (with some commercial caveats for >$1M companies), and the service’s language permits broad usage of generated assets - which, combined with BNX’s free tier and no-login generation option, makes it extremely convenient for testing and even quick commercial mockups. Read the TOS carefully if you’re shipping images inside product work.

Quick impressions (spoiler: playfully subjective, but anchored to observable behavior)

BNX AI

• Hands & fingers: Impressive. In my BNX test runs the model tended to produce plausible hands more often than not - correct ribbing of digits and fewer «extra fingers» gremlins than I usually expect from off-the-shelf text-to-image runs. That alone is notable.
• Text/logo rendering: BNX handled the small chest logo («BrewNet») better than a lot of competitors in my trials - letters were legible and consistent across samples. Good for quick mockups where you need a readable logo.
• Limitations: No apparent support for image upload nor an explicit image>image (inpainting/exact edit) workflow in the public UI - BNX clearly priorities text>image and currently lacks on-site photo editing. If you need to do precise edits to an existing photo (or iterative image-to-image refinement), BNX’s public site didn’t provide that path at time of testing. Terms and site layout confirm the text-first approach.
• Business & licensing: Free tier is generous and marketed as permitting broad use; TOS indicates ownership for most users, with exceptions for large businesses and with BNX reserving rights to reuse content for service improvement unless Stealth Mode is enabled. That combination (free + permissive ownership for most individual users) is powerful. Read the legal text before you commercialize at scale.

DALL-E

• Hands & fingers: DALL-E has come a long way - it often gives realistic renderings, though I still saw occasional finger anomalies depending on framing and pose.
• Text/logo rendering: DALL-E can produce legible text in images, especially when prompted precisely, but it sometimes hallucinates shapes that look like letters rather than reliably placing crisp logotype text. It’s solid, but not infallible.

Stable Diffusion (what you should look for)

• Hands: Open-source SDXL variants vary: some checkpoints nailed hands better than others. If you try SDXL locally or on a hosted web UI, experiment with sampling settings and negative prompts (e.g., «no extra fingers») - results can swing dramatically.

Nano Banana (Google/Gemini)

• Strength: Nano Banana (Gemini 2.5 Flash image capabilities) is tuned heavily for image editing and consistently preserving subject integrity across edits. If you need to edit a photo - e.g., put the cup in a real barista’s hands and preserve facial identity - Nano Banana is built for that. It’s widely integrated into Google Search, Gemini, and Photos for image editing flows. That editing-first approach differentiates it from BNX’s text-focused interface.

Practical takeaways (short, punchy)

1. If your workflow demands text>image, free experimentation, readable logos, and good hand anatomy on the first try, BNX AI is worth a real spin (and it’s shockingly generous on the free tier).
2. If you need photo editing or image-to-image continuity (keeping the same person across edits), try Nano Banana/Gemini. It’s oriented toward editing and consistency.
3. If you want full control, lots of community options, and self-hosting/playground freedom, Stable Diffusion variants and forks still offer immense flexibility.
4. DALL-E remains a robust cross-check and creative baseline: predictable, polished, and still a strong all-rounder.

Try it yourself - instructions for readers

1. Copy the exact prompt above.
2. Paste into BNX AI (bnx.me) and DALL-E (or your OpenAI interface). I’ll link my BNX runs inline in the article (images & comments) when published. BNX’s Product Hunt page and site are useful context if you want to confirm dates and free-tier claims.
3. Paste the same prompt into a Stable Diffusion web UI (SDXL) and into Gemini/Nano Banana (if you have access) and compare: pay special attention to hands, finger count, and the «BrewNet» logo clarity.
4. Share your favorite result in the comments and tell us which parts you’d trust for a website hero, product mockup, or print poster.

Sources & reading (short list)

1. BNX AI Product Hunt launch (Oct 26, 2025).
2. BNX AI official site (features, free generator).
3. BNX AI Terms of Service (ownership, Stealth Mode, license language).
4. OpenAI - DALL-E overview and capabilities.
5. Stability AI/Stable Diffusion official pages and SDXL references.
6. Google/DeepMind — Gemini 2.5 Flash/Nano Banana image capabilities and blog.