Nano Banana, Seedream, Flux: which AI image model to use when

There's no single best AI image model, only the best model for the job in front of you. As of mid-2026: Nano Banana leads on editing and subject consistency, Seedream on high-resolution realism and text rendering, and Flux on prompt adherence with the widest speed-and-cost range. Here's how to choose, job by job.

These three families sit at the top of most quality comparisons right now, which is why we've scoped this guide to them. For the wider field (Midjourney, GPT-image, Ideogram, Firefly), see our roundup of the best AI image generators for marketing teams.

Which model for which job, at a glance

A caveat that applies to every row: these placements reflect mid-2026 versions, and all three vendors ship major updates every few months. Treat the table as a snapshot, not scripture. More on that at the end. And remember that "first pick" means "start here," not "never test the others": for any asset that matters, generating the same brief across two families and comparing is cheap insurance.

Which model is best for photorealistic product scenes?

Start with Seedream. Its high-resolution output and photorealism are the reason it climbed the quality comparisons, and product-in-environment scenes (the watch on the marble counter, the sneaker on wet asphalt) are where that shows. Flux's pro-tier models are the close second, with a particular strength in prompt adherence: when your brief specifies camera angle, lighting, and surface, Flux tends to follow all three instead of two.

One important fork in this job: if the product must be your actual product, pixel-accurate, don't generate the product at all. Generate or photograph it once, then use an editing model to place it into new scenes. That brings us to Nano Banana below. Generating "a bottle like ours" from scratch is how labels warp and proportions drift.

Which model handles text in images best?

Seedream, with newer Nano Banana versions close behind. Text rendering, meaning real words on labels, posters, packaging, and social graphics, was the most reliable embarrassment in AI imagery for years, and these two families have largely fixed it. Seedream's edge extends to non-English text, which matters the moment a campaign is localized. ByteDance's own notes for Seedream 4.5 call out enhanced typography and dense text rendering, including multilingual content and small fonts.

Practical advice regardless of model: keep the text short, put the exact wording in quotes in your prompt, and review every character before anything ships. Models that spell correctly nine times out of ten still need a human on the tenth. For extreme typography work, a specialist like Ideogram remains worth a look. It's in our wider roundup.

Which model keeps characters and people consistent?

Nano Banana is the standout. Its editing-first design means it's notably good at keeping a person, mascot, or product recognizably the same across different scenes, outfits, and angles. That's the job behind brand mascots, recurring campaign characters, and founder-led content. Give it a reference image and a change instruction, and what shouldn't change mostly doesn't. Google's own Gemini API docs document reference-image support built specifically for keeping characters consistent across generations.

Flux Kontext is the strong alternative, taking an input image plus an instruction and applying the change in context. If you're already on Flux infrastructure for volume work, staying in the family for consistency jobs keeps your stack simpler.

The honest limit: "mostly consistent" is the current state of the art, not "identical." Faces and logos drift in small ways across long series. Plan for a human pass on anything where the character is the brand.

Should you edit an existing image or generate a fresh one?

Decide this before picking a model. It changes the answer.

Edit when something in the image is non-negotiable: your product, your packaging, a real person, a shot you already paid for. Editing models (Nano Banana, Flux Kontext, Seedream's edit variants) change the scene, lighting, or background while preserving the subject. This is the right tool for seasonal refreshes of the same product shot, background swaps, and localization passes.

Generate fresh when nothing is sacred: concepts, mood imagery, abstract backgrounds, lifestyle scenes without your product in frame. Fresh generation gives the model full freedom, which usually means better composition than an edit stretched too far.

The common mistake is over-editing: pushing an edit model through five successive revisions until artifacts pile up. If the third edit still isn't right, in our experience it's faster to regenerate from a better prompt than to keep patching.

Which models are fastest and cheapest for high-volume variants?

Every family now ships speed tiers, and the pattern for volume work is the same everywhere: draft cheap, finish expensive.

• Flux's fast variants are the workhorse pick: low cost per image, quick turnaround, quality that's plenty for ad-variant testing and social volume.
• Seedream's lite variants play the same role inside its family.
• Nano Banana is fast by heritage and sits comfortably in the middle.

The workflow that follows from this: generate your twenty layout-and-concept variants on a fast tier, pick the two or three winners, then rerun just those through a pro-tier model for the final asset. Pricing across all three families is per-generation through their APIs and platforms, so the fast-tier-first habit compounds. Most of your generations are drafts, so most of your spend should be draft-priced.

One thing speed tiers don't change: review effort. A hundred cheap variants still need a human to pick winners, so pair high-volume generation with a tight brief. Otherwise you've just moved the bottleneck from generation to selection.

Models leapfrog each other, so build for swappability

Here's the part of this guide that will still be true next year: some of the specific placements above won't be.

These three families have traded the lead repeatedly, and each major release reshuffles at least one row of the table. Chasing each release by hand means rebuilding prompts, re-teaching teammates, and re-validating output every few months, which quietly eats the time the models saved you.

The durable skill isn't picking today's winner. It's building a workflow where the model is a swappable slot: your prompts, reference images, brand rules, and steps stay fixed, and when a better model ships, you swap one slot and rerun. Teams that work this way upgrade in minutes; teams welded to one model's app start over. We've written a full guide to building an AI content workflow around this idea.

How this looks in Orisu

Orisu is built around exactly that swappability. The canvas treats each generation step as a node where the model (Nano Banana, Seedream, Flux, and 100+ others under one subscription) is a setting, not a commitment. Swap the model on one node and rerun; only the changed steps recompute, so trying a new release on yesterday's workflow costs one generation, not a rebuild.

The brand layer rides along too: your brand kit, extracted from your website URL, is applied to every generation regardless of which model runs it, so a model swap never means re-teaching your brand. (That part matters more than the model choice itself; here's how to keep AI images and video on-brand.)

Pick models per job, hold the workflow constant, and let the leaderboard churn.