GPT Image 2 (OpenAI, April 2026) wins on text rendering, structural precision, and 4K output. Nano Banana 2 (Google, February 2026 — officially Gemini 3.1 Flash Image) wins on photorealism, 3–5 second generation speed, and 5-character consistency. Choose GPT Image 2 for marketing creatives with typography. Choose Nano Banana 2 for product imagery and image-to-video pipelines.
This is the side-by-side comparison both vendors make hard to do directly. Both models live on LoveGen AI under one credit balance, so this guide evaluates them on the dimensions that matter for production work — typography, photorealism, speed, character consistency, multilingual support, and how their outputs hold up when fed into video models like Veo 3.1 or Kling 3.0.
The article also addresses the urgent migration deadline most "vs" comparisons skip: DALL-E 2 and DALL-E 3 retire on May 12, 2026 — nine days from publication. Existing DALL-E 3 integrations need a successor. GPT Image 2 is OpenAI's official replacement, but Nano Banana 2 is increasingly the better default for many workflows.
At a glance — which model wins which job
| If your job is… | Pick |
|---|---|
| Marketing creative with embedded copy | GPT Image 2 |
| Product photography / e-commerce mockups | Nano Banana 2 |
| Hero image to feed into Veo 3.1 or Kling 3.0 | Nano Banana 2 |
| UI mockups with crisp typography | GPT Image 2 (or Ideogram 3) |
| Multi-character storyboard with continuity | Nano Banana 2 |
| Heavy CJK / Arabic typography | Qwen Image |
| Cinematic illustration with painterly mood | Seedream 4 |
| Brand-consistent artistic control | Flux 2 Pro |
Two flagships are not the same as two equally good flagships at every job. The decision matrix above is the short version — the rest of this article is the why.
What is GPT Image 2?
GPT Image 2 is OpenAI's image generation model released on April 21, 2026, alongside the consumer-facing ChatGPT Images 2.0 rebrand. It is the first OpenAI image model with built-in reasoning — what OpenAI calls "thinking mode" — and the first to natively support up to 4K output. The model handles small text, iconography, UI elements, dense compositions, and stylistic constraints with a level of precision earlier OpenAI models could not approach.
Release and what it replaces
GPT Image 2 supersedes DALL-E 2 and DALL-E 3, both of which retire on May 12, 2026. Developers running existing DALL-E 3 integrations need to migrate before that date. Most of the migration is changing the model identifier in API calls, but prompt patterns also shift since GPT Image 2 responds differently to compositional instructions.
"Thinking mode" — what reasoning adds
Before any pixels are produced, GPT Image 2 plans the layout, can search the web for visual references, and self-checks its output against the prompt. This costs latency — image generation is no longer near-instant — but it improves prompt adherence, especially for complex briefs with multiple subjects, embedded text, or specific spatial logic. OpenAI reports 99% typography accuracy across dense compositions as a direct result.
Where it's available
GPT Image 2 is available through the OpenAI API, in Codex, on Microsoft Foundry, and on LoveGen AI's GPT Image 2 page. On LoveGen, it runs under the same credit system as every other image model, which makes side-by-side testing against Nano Banana 2 a single-tab workflow.
What is Nano Banana 2?
Nano Banana 2 is Google DeepMind's image generation model released on February 26, 2026. Its official name is Gemini 3.1 Flash Image. The product priority is speed — 3 to 5 seconds per image is typical — without giving up the photorealistic quality of Nano Banana Pro. Google has now made it the default image generator across Gemini, Google Search, Google Ads, and Google Flow.
Release and lineage
The Nano Banana family started as the lightweight image route inside Gemini. The original Nano Banana prioritized speed at modest quality. Nano Banana Pro raised quality at the cost of speed. Nano Banana 2 collapses that tradeoff: Pro-level fidelity at Flash latency. Within Google's stack, it is now the default model for image generation in the Gemini app and in the Flow video editing tool.
Headline feature — Flash speed plus photorealism
Two capabilities define Nano Banana 2 in production: generation speed (3–5 seconds is the typical observed range) and photorealistic naturalism in lighting, materials, and skin texture. The model also maintains character consistency across up to 5 characters and 14 objects in one workflow, which Google designed for storyboarding and multi-shot creative briefs. Personal Intelligence integration in the Gemini app lets users ground generated images in their own Google Photos library.
How to access Nano Banana 2
Nano Banana 2 ships through the Gemini API, the Gemini app, Google Search, Google Ads, Google Flow, and on LoveGen AI's Nano Banana 2 page. On LoveGen, it shares the same credit-based access as GPT Image 2, which means you can test the same prompt on both flagships in two browser tabs without setting up separate Google and OpenAI billing.
Feature-by-feature comparison
The headline matrix below summarizes every dimension that matters for a buying decision. The subsections after the table go deeper on the four areas where the choice is consequential: text rendering, photorealism, speed, and character consistency.
| Capability | GPT Image 2 | Nano Banana 2 |
|---|---|---|
| Vendor | OpenAI | Google DeepMind |
| Released | April 21, 2026 | February 26, 2026 |
| Official model name | gpt-image-2 | Gemini 3.1 Flash Image |
| Max resolution | Up to 4K (custom dimensions) | High, no official 4K flag |
| Generation speed | Reasoning-aware, slower per image | 3–5 seconds typical |
| Text rendering accuracy | OpenAI claims 99% on dense layouts | Strong, secondary focus |
| Character consistency | Standard | Up to 5 characters + 14 objects |
| Multi-turn editing | Yes (context-aware) | Yes (context-aware) |
| Reasoning / "thinking mode" | Yes (first OpenAI image model) | No |
| Personal context (photo library) | No | Yes via Gemini Personal Intelligence |
| Multilingual rendering | JA, KO, ZH, HI, BN explicitly supported | Strong, no specific language list |
| Replaces | DALL-E 2 / DALL-E 3 (retire May 12, 2026) | Nano Banana / Pro (now default) |
| Pricing on LoveGen | See pricing page | See pricing page |
Text rendering and typography
GPT Image 2 is the safer pick when readable text inside the image matters. OpenAI's training run prioritized small text, dense compositions, and multi-language scripts, and the model holds typography accuracy at up to 2K resolution. Marketing layouts, social cards, infographics, and UI mockups all benefit from this — typography that previously required post-production text overlays can now be generated inline.
Nano Banana 2 produces clean readable text in most cases but is not aiming for the same accuracy ceiling. For text-heavy work where typography is the visual hierarchy itself — wordmark designs, dense poster layouts, content with multiple text blocks at different scales — Ideogram 3 still outperforms both flagships. Ideogram is the typography specialist on LoveGen and remains the right tool for typography-first design.
Photorealism and cinematic lighting
Nano Banana 2 leads when output should look photographed rather than rendered. Cinematic lighting, natural skin texture, realistic material physics (fabric drape, glass refraction, metal reflectivity), and atmospheric depth all show Google's training emphasis on photographic naturalism. Product mockups and editorial photography mockups consistently land closer to "indistinguishable from a real shot" with Nano Banana 2.
GPT Image 2's photorealism is competent but tends toward the cleaner, more illustrated look that suits structured compositions. For painterly cinematic illustration with stronger artistic style, Seedream 4 and Flux 2 Pro remain strong choices on LoveGen — Seedream for narrative cinematic mood, Flux 2 Pro for fine-grained artistic control.
Speed and cost
Generation speed is where Nano Banana 2 has its clearest lead. Typical output time is 3 to 5 seconds, putting it in Flash latency territory. GPT Image 2's thinking mode adds a reasoning step before pixels, which means substantially longer per-image latency — typically several times slower than a Flash-class model, depending on prompt complexity. For workflows that iterate dozens of variants, the speed gap matters.
On cost, both models use credit-based pricing on LoveGen. Per-image credit cost is shown on each model's page and on the pricing page. For raw API pricing, OpenAI and Google rates are comparable per high-quality image, with Nano Banana 2 generally cheaper per image at standard resolutions due to its lower compute footprint.
Character consistency and multi-subject scenes
Nano Banana 2 advertises consistency for up to 5 characters and 14 objects across a single workflow. In practice, this means storyboard sequences and multi-shot creative briefs hold together better — the same character's face, clothing, and props persist across a series of generations without explicit reference images for every shot.
GPT Image 2 handles multi-subject composition well within a single image but does not match Nano Banana 2's multi-frame consistency at this scale. For multi-character storyboards or scene continuity work, Nano Banana 2 is the practical choice.
Multi-turn editing
Both models support context-aware multi-turn editing — generate an image, then ask for a specific change ("swap the jacket to navy", "add a clock to the wall"), and the rest of the image stays consistent. Nano Banana 2 has the additional advantage of Gemini Personal Intelligence integration in the Gemini app: edits can pull context from your own Google Photos library, which is genuinely useful for personal projects but irrelevant for B2B production work.
Multilingual and non-Latin text
GPT Image 2 explicitly supports text rendering in Japanese, Korean, Chinese, Hindi, and Bengali at the same accuracy as English. Nano Banana 2 also handles non-English scripts well, but Google has not published an explicit language list. For the heaviest CJK or Arabic typography work — say, a poster where the entire visual hierarchy is built around Chinese characters — Qwen Image is purpose-built for this and worth testing alongside the flagships.
How they perform in image-to-video pipelines
A static image is rarely the final output today. Most production workflows extend the still into motion via image-to-video pipelines — the still frame becomes the first frame of a Veo 3.1, Kling 3.0, or Seedance 2 generation. The choice of image model affects how cleanly that transition works.
Why the choice of image model matters for video output
Photorealistic stills feed video models more naturally. Atmospheric depth, real-world lighting, and natural material physics are signals video models already understand from their video training data. When the first frame already looks photographed, the motion model has less work to do reconciling the source.
Highly structured or text-heavy compositions are harder. Embedded readable text, sharp geometric layouts, and UI elements often fight the video model — text wobbles, geometry warps, and stylistic precision degrades over the first 1–2 seconds of motion. This is a real artifact, not hypothetical, and it affects both Veo and Kling outputs.
Recommended pairings
For image-to-video first frames, Nano Banana 2 → Veo 3.1 is the most reliable pairing today. The photorealistic naturalism transfers smoothly into Veo's motion synthesis and audio generation. Nano Banana 2 → Kling 3.0 is the right choice for longer clips (Kling supports up to 5 minutes) and multi-shot directing. Nano Banana 2 → Seedance 2 suits creative motion effects.
GPT Image 2 outputs work as video first frames when the brief doesn't depend on embedded text or strict geometric layout. For typography-heavy stills that must stay readable in motion, the better workflow is to generate the still in GPT Image 2 and add motion via post-production rather than via image-to-video.
When LoveGen's other models beat both flagships
GPT Image 2 and Nano Banana 2 are the headline flagships of 2026, but they are not the right answer for every job. Four LoveGen models still outperform them in specific categories:
- Imagen 4 — Google's premium image tier, preferred for highly polished commercial photography mockups where Nano Banana 2's speed-tuned weights leave detail on the table.
- Flux 2 Pro — Black Forest Labs' flagship, the better choice for brand-consistent artistic control. Stylistic adherence to a defined visual identity (color palette, illustration language, character design) is its core strength.
- Seedream 4 — ByteDance's image model, dominant on cinematic illustration and painterly mood. For narrative imagery with atmospheric depth and stylized lighting, it routinely beats both flagships.
- Ideogram 3 — the typography specialist. When the text is the design (logo wordmarks, dense typographic posters), Ideogram 3 still produces cleaner output than GPT Image 2.
The unified LoveGen credit system means trying alternatives doesn't require new accounts or new billing — same credit balance, different model page.
What about DALL-E 3?
DALL-E 2 and DALL-E 3 retire on May 12, 2026 — nine days after this article's publication date. After that, both models are no longer accessible through the OpenAI API, which means any DALL-E 3 integration in production needs migration before then.
GPT Image 2 is OpenAI's official successor. Migration is mostly mechanical — the model identifier changes, and the API parameters are largely compatible. Two practical differences are worth flagging: GPT Image 2's thinking mode adds latency, so any DALL-E 3 workflow that assumed near-instant returns will need to handle longer response times; and GPT Image 2 responds differently to compositional prompts, particularly around embedded text and structured layouts, so prompt templates often need light tuning.
Workflows that don't strictly need OpenAI can also use the migration as a chance to evaluate Nano Banana 2 — for many DALL-E 3 use cases (product imagery, social content, photorealistic creatives), Nano Banana 2 is the better fit.
How to choose — a 5-second decision guide
| If your job is… | Pick |
|---|---|
| Marketing creative with embedded copy | GPT Image 2 |
| Product photography / e-commerce mockups | Nano Banana 2 |
| Hero image to feed into Veo 3.1 or Kling 3.0 | Nano Banana 2 |
| UI mockups with crisp typography | GPT Image 2 (or Ideogram 3) |
| Multi-character storyboard with continuity | Nano Banana 2 |
| Heavy CJK / Arabic typography | Qwen Image |
| Cinematic illustration with painterly mood | Seedream 4 |
| Brand-consistent artistic control | Flux 2 Pro |
| Migrating from DALL-E 3 (production) | GPT Image 2 |
| Migrating from DALL-E 3 (open to alternatives) | Nano Banana 2 |
The full LoveGen catalog of AI image models is the practical place to test these in sequence — same credits, same UI, same prompt history. For the broader AI image generator experience, every model on this list is one click away.
Frequently asked questions
Is GPT Image 2 better than Nano Banana 2?
Neither model is universally better — they specialize. GPT Image 2 wins on typography, structural precision, and 4K output, with OpenAI claiming 99% text rendering accuracy on dense compositions. Nano Banana 2 wins on photorealism, generation speed (3–5 seconds), and character consistency across up to 5 subjects. Choose based on the job. Both are available side-by-side on LoveGen AI.
When was GPT Image 2 released?
GPT Image 2 launched on April 21, 2026, alongside OpenAI's consumer-facing "ChatGPT Images 2.0" rebrand. It is the first OpenAI image model with built-in reasoning ("thinking mode") that plans layout before generating, can pull web references, and self-checks outputs. It replaces DALL-E 2 and DALL-E 3, both retiring on May 12, 2026.
When was Nano Banana 2 released?
Nano Banana 2 launched on February 26, 2026, by Google DeepMind. Its official model name is Gemini 3.1 Flash Image. It is now the default image generation model across Gemini, Google Search, Google Ads, and Google Flow, and combines the quality of Nano Banana Pro with the latency of Gemini Flash.
Does Nano Banana 2 support 4K resolution?
Google has not officially flagged 4K as a default output resolution for Nano Banana 2 — its design priority is speed (3–5 seconds per image) over maximum dimensions. GPT Image 2 explicitly supports up to 4K at custom dimensions. For maximum resolution today, GPT Image 2 is the safer choice; for everything else, Nano Banana 2's quality is competitive at typical web sizes.
What is "thinking mode" in GPT Image 2?
Thinking mode is GPT Image 2's reasoning step that runs before any pixels are generated. The model plans the layout of the image, can perform a web search for visual references, and self-checks the output against the prompt. This is the first time OpenAI has shipped reasoning inside an image model — it improves prompt adherence at the cost of slightly longer generation time.
Can both models edit existing images?
Yes. Both GPT Image 2 and Nano Banana 2 support context-aware multi-turn editing — you can generate an image, then ask for specific changes (object swap, lighting tweak, text correction) while the rest of the image stays consistent. Nano Banana 2 also integrates Google Photos through Gemini Personal Intelligence, allowing edits that reference your own photo library.
Which model is better for marketing visuals with text?
GPT Image 2 is the safer pick for marketing creatives that include readable copy — OpenAI reports 99% typography accuracy across dense compositions, and the model handles non-Latin scripts (Japanese, Korean, Chinese, Hindi, Bengali) at the same precision. For very heavy typography work where the text is the main subject, Ideogram 3 still outperforms both flagships.
Which model is better for photorealism?
Nano Banana 2 leads on photorealism, cinematic lighting, and natural skin and material textures. Google's training emphasis on photographic naturalism shows in the output. For painterly or cinematic illustration with a stronger artistic style, Seedream 4 and Flux 2 Pro are also strong alternatives available on LoveGen AI.
Will DALL-E 3 still work after May 12, 2026?
No. OpenAI has confirmed that DALL-E 2 and DALL-E 3 retire on May 12, 2026, and existing API integrations need to migrate before that date. GPT Image 2 is the official successor, accessible through both the OpenAI API and through LoveGen AI's GPT Image 2 page. Migration mostly requires switching the model identifier and adjusting prompt patterns.
Can I use both GPT Image 2 and Nano Banana 2 on LoveGen AI?
Yes. Both are available on LoveGen AI under one credit balance — GPT Image 2 here and Nano Banana 2 here. This makes side-by-side comparison straightforward without needing separate OpenAI and Google billing relationships. Pricing per image is shown on each model's page and on the pricing page.