Two things can be true at once:
If you’re in the US/EU and you care about getting usable assets fast (marketing, product, UI, docs, ads, thumbnails), this comparison isn’t about vibes. It’s about: what breaks less often.
OpenAI rolled out a new Images experience inside ChatGPT and made the same model available via API as GPT Image 1.5. They’re pitching it as: stronger instruction following, more precise edits that preserve lighting/composition/likeness, improved dense text rendering, and up to 4× faster generation vs the previous version. (OpenAI)
Developers also got API access + published pricing (text + image token pricing) and versioned snapshots like gpt-image-1.5-2025-12-16. (OpenAI Image)
Most coverage frames GPT Image 1.5 as a direct response to Nano Banana Pro’s viral run—especially around “post-production” editing and text handling.
If you’re tempted to crown a winner based on one leaderboard screenshot, don’t.
LMArena’s blind Image Edit Arena (updated Dec 16, 2025) shows chatgpt-image-latest (20251216) narrowly ahead of gemini-3-pro-image-preview-2k (nano-banana-pro) by a few points.
LMArena’s Text-to-Image Arena (also updated Dec 16, 2025) puts gpt-image-1.5 ahead of the 2K Nano Banana Pro variant as well.
Translation: OpenAI caught up fast. But “caught up” is not the same as “dominates in your workflow.”
GPT Image 1.5’s best story is reliability. OpenAI explicitly emphasizes “change only what you ask” and keep everything else stable across edits—lighting, composition, people’s appearance. (OpenAI)
That matters when you’re iterating on:
Nano Banana Pro is also described as “studio-quality” with sophisticated editing, but in my experience it sometimes behaves like an art director with opinions—great when you want polish, annoying when you want strict compliance. The key point is: both are strong; GPT Image 1.5 is clearly optimized for “do exactly this, not more.”
My blunt take: if your prompt reads like a contract, GPT Image 1.5 is the safer bet.
This is where “almost good” is still useless.
OpenAI claims improved dense text rendering and shows examples like converting a markdown-style article layout into an image. (OpenAI)
But Nano Banana Pro has been consistently framed as the model that impressed people specifically for accurate text rendering and “studio-quality visuals.”
My blunt take:
This is the “production” bar.
OpenAI’s announcement is unambiguous: edits should preserve what matters and avoid collateral changes. (OpenAI) TechCrunch’s coverage repeats the same theme—granular edit controls to maintain consistency (likeness, lighting, tone).
Nano Banana Pro is positioned similarly, but with an extra emphasis on high-end compositing and consistency when blending inputs.
My blunt take:
Quartz reports Google’s own billing: Nano Banana Pro can blend complex compositions using up to 14 images and keep consistency for up to five people, plus improved text for posters/diagrams.
OpenAI’s messaging leans more on “preserve likeness across edits” rather than quoting hard caps like “14 images.” (OpenAI)
My blunt take: if your workflow is “here are 10 reference shots, merge them cleanly,” Nano Banana Pro is the one that’s explicitly marketed for that.
A lot of teams are quietly moving to structured prompts because they want repeatable outputs (brand-safe, layout-safe, automation-friendly). So here’s a practical way to compare both models:
You don’t ask for an image with a poetic paragraph. You send layout specs.
Example JSON (simplified):
{
"canvas": { "width": 1024, "height": 1536, "style": "clean editorial poster" },
"grid": { "columns": 12, "margin": 64, "gutter": 16 },
"palette": { "bg": "#0B1220", "accent": "#7C3AED", "text": "#F8FAFC" },
"elements": [
{ "type": "title", "text": "Launch Week", "x": 64, "y": 96, "size": 96, "weight": 800 },
{ "type": "subtitle", "text": "Build. Ship. Iterate.", "x": 64, "y": 220, "size": 40, "weight": 600 },
{ "type": "badge", "text": "No Credit Card", "x": 64, "y": 320, "size": 28 },
{ "type": "cta", "text": "Try it free →", "x": 64, "y": 1250, "size": 44, "weight": 800 }
],
"rules": [
"Do not change wording.",
"Text must be perfectly legible.",
"Keep spacing consistent with the grid."
]
}
If you’re building tooling (templates, brand kits, automated creatives), JSON-ish prompting is how you stop playing “prompt roulette.” In that world, the “winner” is whichever model violates the spec less often.
OpenAI has clear API pricing published for GPT Image 1.5 (text + image tokens) and snapshot versioning for stability. (OpenAI Image)
OpenAI also claims image inputs/outputs are 20% cheaper vs GPT Image 1 (useful if you’re doing volume). (OpenAI)
For Nano Banana Pro, the cost story is less consistent across sources this week (and often tied to Google product surfaces), so I won’t pretend you can spreadsheet it from headlines.
If you only want one model: use GPT Image 1.5 for structured, iterative workflows and switch to Nano Banana Pro when text or compositing becomes the failure point. That’s the most honest “production” advice based on what’s actually being emphasized in Dec 16–17 coverage and benchmarks. (OpenAI)
Would you like me to help you draft a specific prompt structure for testing the "instruction following" capabilities mentioned in the article?
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