Pick the right model vs Midjourney / Flux / Nano Banana / DALL-E 3

Which one for Xiaohongshu covers? Which one for event KV posters? Which one for art illustrations? Anyone who actually creates content knows one thing — there's no "best model". Only "best for the task".

gpt-image-2 hit #1 on Image Arena 12 hours after launch, leading second place by 242 points (the largest gap ever). But that doesn't mean it's the first pick for every scenario. Midjourney v7 still has a slight edge on artistic vibe, Flux 1.1 Pro is more reliable for photorealistic portraits, and Nano Banana's free tier is enough for solo creators.

This chapter puts all five mainstream models on the table and gives you a comparison sheet you can use right away — plus a decision tree.

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1. Core Comparison Matrix

Data reflects 2026-04. Image Arena rankings shift every six months, but the gap on "text / Reasoning / multi-turn editing" won't flip anytime soon.

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2. Decision Tree (task-driven model picking)

任务里有大量文字（标题/中文/Logo）吗?
├─ 是 → gpt-image-2
└─ 否 → 要极致艺术氛围 / 电影感吗?
        ├─ 是 → Midjourney v7
        └─ 否 → 要极致写实人像（产品 / 真人摄影）?
                ├─ 是 → Flux 1.1 Pro
                └─ 否 → 预算敏感 / 个人玩 → Nano Banana

注：DALL-E 3 已停用，老项目还在调用的，迁到 gpt-image-2。

Can't memorize the matrix? One line is enough:

• gpt-image-2 = the obedient generalist (reasoning + text + multi-turn)
• Midjourney v7 = the vibe master (cinematic feel maxed out, but butchers Chinese text)
• Flux 1.1 Pro = the technical realist (skin and hair are its comfort zone)
• Nano Banana = free and good enough (solo / proof of concept)
• DALL-E 3 = history (replaced by gpt-image-2)

Here's an analogy. Midjourney is like a vibe DJ — atmosphere cranked to eleven, but it doesn't always play what you asked for. gpt-image-2 is like an obedient coworker — you say it, it does it, including writing nine Chinese characters like "30 天学会 ChatGPT" stroke-perfect onto the image.

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3. Real-World Scenario Picks

Scenario 1: Xiaohongshu cover (Chinese title + photoreal feel)

I'd pick gpt-image-2. Not because the quality is "highest" — because it eliminates the "drop the Chinese title in by hand" step. In the MJ era, every cover meant opening Photoshop to add text. Three covers, 30 minutes gone. gpt-image-2 spits out 8 candidates in one go, with the title already on the image, and it's correct.

Scenario 2: Event KV poster (Chinese slogan + clean space for the logo)

Still gpt-image-2. You can explicitly tell it "leave a clean 200×100 area top-left for the logo" — other models will probably ignore that kind of instruction.

Scenario 3: Art illustrations / concept art / magazine spreads

Midjourney v7. This is MJ's real moat — the one that hasn't been replaced. Same prompt: gpt-image-2 comes out feeling "commercial", MJ comes out with that hard-to-pin-down "soul" — light layering and compositional instinct closer to a top-tier photographer.

Scenario 4: Photorealistic portraits (e-commerce, product shots, models)

Flux 1.1 Pro. Skin texture, hair strands, hand details — all the uncanny valley landmines. Flux handles them cleanest. gpt-image-2 still occasionally drops the old "6 fingers" bug.

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4. Combo Workflows (this is where it gets interesting)

In practice we almost never pick "one or the other". It's all three together.

A real example. Last month we did a key visual for an "AI Bootcamp" event:

1. gpt-image-2 for the base image — Chinese title "AI 训练营" and subtitle "30 天从 0 到部署" nailed in one shot, text accurate, logo space clean
2. Midjourney for art variants — same brief, MJ does a moodier version, picked one as an A/B test creative for ad spend
3. Flux for portrait detail — there's a "developer typing on keyboard" close-up in the KV, generated separately with Flux and composited in

These three aren't replacements for each other. They're "three different knives in the toolbox". Once you know how to use each one, your content output gap with everyone else widens fast.

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5. Things That Went Wrong

Wreck 1: Using Midjourney for a Chinese poster

A designer friend insisted MJ has the "best vibe". Nine characters of "AI 工程师训练营" on the event KV — all wrong. "训" missing strokes, "师" rendered as some Japanese katakana mishmash. Ended up wiping it in Photoshop and retyping, 15 extra minutes per image. Takeaway: let MJ do the base, but never let it write Chinese. Do the text layer in Photoshop / Photopea / gpt-image-2.

Wreck 2: Using gpt-image-2 for art illustrations

Asked gpt-image-2 for a "cyberpunk Tokyo street at night". Composition was right — but the tone leaned "ad shoot commercial", not the slightly grainy cinematic feel MJ has. Takeaway: pure mood / art / concept goes back to MJ. Don't force gpt-image-2.

Wreck 3: Using Nano Banana for commercial assets

A teammate used Nano Banana for free and made an e-commerce hero set for a client. Client legal checked the commercial license — blocked it. Takeaway: Nano Banana fits solo use / internal demos / proof of concept. Read the actual license before commercial use. Don't assume.

Wreck 4: DALL-E 3 still in production code

One legacy project still had model: "dall-e-3" API calls. After May 2026 that endpoint will be sunset. Takeaway: replace dall-e-3 with gpt-image-2 in your codebase today. Parameters are compatible (size / quality map across). Don't wait for a 500 in production.

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6. Our Real Production Numbers

JR Academy ran 200 images through over 4 weeks as a baseline (covers / posters / course banners / WeChat Moments graphics). The split across three models:

Not because gpt-image-2 is "3× better" than MJ — it's because 70% of our content is Chinese + text-heavy. If you run a wallpaper account / art blog / photography community, the ratio flips. MJ at 70% is totally normal there.

Picking a model isn't about checking a leaderboard. It's about what your content actually looks like.

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7. What's Next

The next chapter is the quickstart — how to get going with gpt-image-2, 5 minutes from account signup to first usable image. Covers the differences between ChatGPT Plus / Codex / API entry points, how to write your first prompt, and a few compliant access paths from inside China.

If you already have an account and want to skip ahead:

1. Take the "Real-World Scenario Picks" in §3 and find the closest match
2. Pick your main model (probably gpt-image-2)
3. Jump to Ch 03 quickstart and ship your first image in 5 minutes
4. Come back to §2 decision tree later to figure out when to switch to MJ / Flux

More models isn't better. Get one knife sharp first. Then start combining.

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📷 Real Comparison Cases

The real output comparisons below are curated from awesome-gpt-image (CC BY 4.0). A direct look at the gap between models and across generations.

Case 1: GTA San Andreas screenshot — GPT Image 1.5 vs gpt-image-2

Prompt:

gameplay screenshot of a lion fighting against an npc in gta san andreas

The original poster's take: 1.5 was "wrong art style, fake UI, looks like a low-quality GTA mod", while gpt-image-2 "just looks the way it's supposed to look". Same prompt, two generations apart — and a clear answer to why "old DALL-E 3 calls need migrating now".

📷 Creator: @flowersslop · Curated by: awesome-gpt-image

Case 2: 90s point-and-shoot quality

Prompt:

90s + point-and-shoot camera quality

A 7-word prompt produces 4 images with a consistent "90s disposable camera" feel. That "short prompt + strong style anchor" capability is hard to reproduce stably outside of Midjourney — you need both the style-word weighting and the reasoning. Neither is optional.

📷 Creator: @sunyunran · Curated by: awesome-gpt-image