GPT-Image 2.6 Sunburst vs Nano Banana Pro: Which Flagship Image Model Should You Use?

On November 4, 2026, OpenAI released Images 2.6, a major upgrade to its image generation lineup. People now create 4 billion images per week across ChatGPT, Codex, and the API, according to OpenAI, and the new release brings two distinct models, animated GIFs, SVG export, and a new way to point at exactly what should change in an edit.

The timing sets up the clearest head-to-head in image generation right now. On the other side stands Google's Nano Banana Pro, formally Gemini 3 Pro Image, its premium image engine since November 2025. Both generate and edit images from plain-language prompts, both claim best-in-class text rendering, and both cost real money per image. They differ in almost everything else.

This comparison looks at what each model actually does, what it costs, and which one fits which job.

The two contenders at a glance

Speed: Flare is fast, Pro takes its time

Speed is where the lineups split most clearly. OpenAI says Images 2.6 generates images 50% faster than before, although it does not say what that figure is measured against, so treat it as the company's own claim. The concrete number is for Flare: at low quality, it can generate an image in as little as six seconds. Sunburst is slower by design, trading seconds for detail and steadier edits.

Nano Banana Pro takes the opposite approach. Google's image guide says its Gemini 3 image models run a reasoning step on complex prompts, generating up to two interim images to test composition and logic before producing the final one. That deliberation is part of why Pro renders dense layouts well, but it also means a call takes longer, and the thinking tokens are billed by default, so the price of a call is more than the per-image figure alone. Google publishes no speed figure for Pro at all.

In practice: if you iterate quickly — dozens of variations, rough drafts, social posts — Flare's speed is the most noticeable upgrade here. If you generate a handful of final assets and can wait, Pro's slower pace is a fair trade.

Text rendering and image quality

Both companies lead with the same promise: words inside images that come out spelled right. Google made the claim first and loudest, calling Nano Banana Pro the best model for correctly rendered and legible text, whether a short tagline or a long paragraph, and its model page still points Pro at complex graphic design, product mockups, and data visualizations that need accurate text.

OpenAI's answer arrived with the arena numbers. Within days of launch, Sunburst had climbed to the top of Artificial Analysis's public text-to-image leaderboard, with Flare close behind — an independent blind-vote ranking, not a vendor benchmark, and a sign of how narrow the quality gap between the two flagships has become.

Resolution is where Pro still holds a clear edge. It renders at 1K, 2K, and 4K, and 4K matters when an image ends up on a poster, a slide deck, or a print layout. OpenAI, by contrast, sells quality levels — low, medium, high, and max — rather than pixel sizes, and does not advertise a 4K tier. For screen-only work, that difference rarely matters. For print, it decides the pick.

Editing and staying consistent

Image models are judged as much on the second, third, and tenth edit as on the first generation, and here each side has its own system.

Sunburst's pitch is consistency across edits. OpenAI says it brings greater image consistency when quality is set to medium or high — the setting that matters for product shots and brand assets that must look the same from one revision to the next. In ChatGPT, Images 2.6 adds saved reference images, so a character, product, or brand detail can be reused across sessions. In the API, a new markup-guided edit lets you draw on a transparent layer to show the model exactly where to change something.

Pro's answer is volume of references. Google's guide says the Nano Banana models accept up to 14 reference images, split across objects, characters, and style references. Pro keeps up to five people recognizable across scenes, which is the hardest consistency problem in this category, and it supports multi-turn editing, so you can refine one image step by step. Its character consistency is the reason advertisers reach for it when a campaign needs the same person, product, and logo across many variants.

Neither side wins outright: Sunburst's markup edits are the more surgical tool for fixing one detail, while Pro's reference budget is stronger for holding a whole cast and a brand look together.

What only one of them can do

Each model has two tricks the other cannot match, and they point at different users.

On OpenAI's side, the new formats stand out. Images 2.6 can turn a prompt or an image into an animated GIF — a looping mascot, a reaction, a meme, or a storyboard — and it can export static SVG files, so logos and icons are usable on websites, in apps, and in print without a separate conversion step. A new complexity parameter also lets developers choose how much planning the model does before it renders.

On Google's side, the differentiator is grounding. Pro can use Google Search grounding when it generates, pulling in real-time facts such as weather, stock charts, or recent events. That serves charts and data visuals better than product shots, and it is a capability OpenAI does not offer. Pro also powers Flow, Google Ads Asset Studio, and integrations with Adobe and Figma, shortening the path from a generated image to a finished ad.

Pricing: tokens against flat rates

The two companies also bill differently, and the math favors different users.

OpenAI charges per token: $5 per million text input tokens, $8 per million image input tokens, and $30 per million image output tokens, the same for Sunburst and Flare. Its own calculator puts a 1024×1024 image at roughly $0.02 at low quality, $0.04 at medium, and $0.05 at high, with the top max setting costing around $0.21. The bill depends on your prompt, settings, and retries, so budget per accepted image rather than per attempt.

Google charges a flat rate per image: $0.134 for a 1K or 2K image and $0.24 for a 4K image through the Gemini API, with no free tier for the API at all — and the thinking tokens Pro uses are billed on top, so the headline price understates a complex call. In the Gemini app, Pro is bundled with Google AI plans (Plus $4.99, Pro $19.99, Ultra from $99.99 a month), with limits rising by tier. OpenAI, meanwhile, made Images 2.6 available in ChatGPT and Codex across all subscription tiers, including free, from launch day.

At everyday sizes, OpenAI's medium quality is the cheaper image; at 4K, Google is the only one of the two selling it. If price decides it, Google's own Nano Banana 2.1 undercuts both at $0.0336 per 1K image, and Google recommends it for new projects where Pro's extra fidelity is not needed.

Where you actually use them

Most people will meet these models inside a chat app, not through an API. Images 2.6 is live in ChatGPT and Codex, where a prompt or an uploaded photo is enough to start generating, and both Sunburst and Flare are available to developers through the Images API.

Nano Banana Pro is the image model inside the Gemini app, where it appears as a redo option for images that need more detail, and it is available to developers through the Gemini API, Google AI Studio, and Vertex AI. Third-party hosts such as fal and Replicate also sell it, which is useful if your stack already runs there.

The ecosystem difference is worth noting. OpenAI's models travel well: ChatGPT, Codex, and one API cover consumers and developers alike. Google's travel further inside Google's own world, where Ads, Flow, and creative platforms put the model where campaigns are already made.

Verdict: who should pick which

Pick GPT-Image 2.6 if you live in ChatGPT, need animated GIFs or SVG output, iterate fast and want Flare's six-second drafts, or build with an API where token-level pricing and markup-guided edits fit your workflow. Sunburst is the right choice inside that pair when an image has to survive repeated edits without drifting.

Pick Nano Banana Pro if you need 4K output, dense layouts with a lot of text, Search-grounded charts and data visuals, or up to 14 reference images holding a person, a product, and a style together across a campaign. It is also the natural pick if your work already runs through Google Ads or Flow.

For everyone else, both are now good enough that the workflow matters more than the model. The real choice is between OpenAI's speed and formats on one side and Google's resolution, grounding, and reference budget on the other — and the job will tell you which side to stand on.

Sources

• OpenAI — openai․com
• OpenAI API Pricing — developers․openai․com
• Artificial Analysis — artificialanalysis․ai
• Novoads — novoads․ai