Nano Banana 2 vs GPT Image 1.5: OpenAI vs Google images
By the Infer teamUpdated
Nano Banana 2 is the better default for most image work in 2026: it generates in about 1.4 seconds against GPT Image 1.5's roughly 5 seconds (about 3x faster), legibly renders text in 30+ languages including non-Latin scripts, holds up to 5 characters consistent across scenes, and costs a hair less per image ($0.039 vs. $0.04) (tryinfer.com/models/nano-banana-2, tryinfer.com/models/gpt-image-1-5). GPT Image 1.5 wins the scenario where it actually counts: complex, multi-subject photorealistic composition. Infer's benchmark copy puts it 12% ahead of FLUX 1.1 [pro] on photorealism and calls it "Top 1" for portrait fidelity, with the headroom to hold together scenes of 8 or more elements that trip up faster models. It also edges Nano Banana 2 on Infer's own leaderboard, 1271 Elo to 1262. If the job is a packed catalog scene or an editorial portrait, use GPT Image 1.5. For character work, multilingual campaigns, or anything where speed matters, use Nano Banana 2.
Infer hosts both models and profits the same either way; this ranking is just what the leaderboard and each model's documented specs show.
Spec comparison
| Developer | OpenAI | |
| Latest version | GPT Image 1.5 | Nano Banana 2 |
| Modality | Text-to-image / editing | Image generation & editing |
| Max resolution | Not specified on Infer's page | 1024×1024 (primary spec listed) |
| Max duration | ||
Sources: tryinfer.com/models/gpt-image-1-5, tryinfer.com/models/nano-banana-2, tryinfer.com/leaderboards. One caveat: Infer's full image-leaderboard table loads client-side and wasn't fully capturable in our latest data pull, so the 1271/1262 Elo figures above come from each model's individual page, not a verified head-to-head snapshot. Treat the gap as directional rather than exact. Artificial Analysis runs an independent text-to-image arena that can diverge from Infer's numbers on any given day; the two shouldn't be blended into one figure.
Scenario breakdown
Eight-element scene. A crowded frame, a market stall with a vendor, several customers, a cash register, a fruit display, a hanging scale, an awning, and a price sign, is exactly what GPT Image 1.5's multi-subject composition claim targets: Infer's benchmark copy credits it with holding together scenes of 8 or more elements that trip up faster models. Nano Banana 2's documented use cases skew toward character consistency and localized content rather than dense multi-subject staging, and Infer doesn't make an equivalent claim for it at this element count. On the specs, this scenario favors GPT Image 1.5.
Non-Latin poster text. A poster built around Japanese vertical typography is Nano Banana 2's home turf on paper: it's documented to legibly render 30+ languages including non-Latin scripts, and Infer's own copy calls it "Ideogram-grade text fidelity at half the latency." GPT Image 1.5 is positioned by Infer as a "strong second to Ideogram," not a text specialist, so a demanding non-Latin typography brief favors Nano Banana 2 by its published spec.
Same character across three scenes. Nano Banana 2's documented spec is up to 5-character consistency across scenes, which covers exactly this brief. GPT Image 1.5's Infer page doesn't publish an equivalent multi-scene consistency number, so treat that as an open question rather than a confirmed weakness, but the published spec favors Nano Banana 2 here.
Run this prompt with Nano Banana 2 on Infer → · Test it with GPT Image 1.5 on Infer →
Where GPT Image 1.5 wins
- Dense, multi-subject scenes. Infer's benchmark copy credits it with holding together scenes of 8 or more elements, the threshold where faster models tend to start merging objects.
- Photorealism. Infer's own benchmark puts it 12% ahead of FLUX 1.1 [pro] on photorealistic output, and "Top 1" on portrait fidelity specifically.
- Documented rate limits at scale. 60 req/min default, bursting to 120, scaling to 1,000+ rpm on paid tiers: a published ceiling you can plan a production pipeline against.
- Scientific and editorial diagrams. Listed directly among Infer's use cases, alongside editorial portraits and product staging.
Where Nano Banana 2 wins
- Speed. ~1.4 second median latency (P95 ~2.6s) versus GPT Image 1.5's ~5 seconds, roughly 3x, and it compounds fast across a batch.
- Multilingual text. 30+ languages, including non-Latin scripts, render legibly per Infer's documented spec, against GPT Image 1.5's positioning as a "strong second to Ideogram," not a text specialist.
- Character consistency. Up to 5 characters held stable across scenes, per Infer's published spec, with no equivalent number documented for GPT Image 1.5.
- Price. $0.039/image versus $0.04/image: small per unit, but it adds up at catalog scale, and it's the cheaper option even before the time savings.
Pricing reality
A 500-image batch costs $20.00 with GPT Image 1.5 at its flat $0.04/image rate, and $19.50 with Nano Banana 2 at $0.039/image: a $0.50 difference that won't move a budget decision. Time is the real gap: at ~5 seconds per image, GPT Image 1.5 takes roughly 42 minutes to clear that batch; at ~1.4 seconds, Nano Banana 2 clears it in about 12 minutes. Off Infer, Google's own Gemini API prices Nano Banana 2 by resolution tier rather than flat per-image. Third-party aggregators put it at roughly $0.045 (512px) to $0.151 (4096px), unverified against Google's own pricing docs, so Infer's flat $0.039 rate is simpler to budget against regardless of output size. See more cost breakdowns on Infer's pricing hub.
The verdict
Choose GPT Image 1.5 when a single frame has to hold a lot: crowded scenes, editorial portraits, or anything where photorealistic composition is the whole ask. Choose Nano Banana 2 for everything else: multilingual campaigns, character-driven storytelling, or a catalog job where 3x the speed at a slightly lower price compounds into real time and cost saved. And if the job is pure typography, a poster or logo where the text is the point, neither of these is the right call. Ideogram 3.0 is Infer's dedicated typography model at $0.06/image, and prompt 2 above is exactly the kind of job it's built for.
Try both side-by-side on Infer →
See also: the best text-to-image models in 2026, ranked, Kling 3.0 Pro vs Veo 3.1 Fast, Kling 3.0 Pro vs Seedance 2.0 Pro, Wan vs Kling vs Hailuo, and the compare hub for every head-to-head we've run.
Frequently asked questions
Which model renders text better, GPT Image 1.5 or Nano Banana 2?
Nano Banana 2. It legibly renders 30+ languages including non-Latin scripts, and Infer's own copy describes it as delivering 'Ideogram-grade text fidelity at half the latency.' GPT Image 1.5 handles text as a secondary strength, positioned by Infer as a 'strong second to Ideogram,' but it isn't built around multilingual typography the way Nano Banana 2 is. If typography is the entire job, Ideogram 3.0 ($0.06/image on Infer) still beats both.
Which model holds character consistency across multiple scenes?
Nano Banana 2 documents consistency for up to 5 characters across scenes, which is the spec Infer lists for it directly. GPT Image 1.5's Infer page doesn't publish an equivalent multi-scene character-consistency number, so treat that as an open question rather than a documented weakness. It may hold up in testing, but there's no published figure to cite.
Can both models edit existing images, not just generate new ones?
Yes, both are listed as generation-and-editing models on Infer. GPT Image 1.5's use cases include editorial photo edits and text-on-image marketing; Nano Banana 2's include character-consistent storytelling and product-photography mockups built from existing assets. Neither Infer page documents a dedicated mask-input parameter the way FLUX.1 Kontext [pro] does, so for heavy inpainting-style edits, Kontext is worth checking first.
What are the API rate limits for each model?
GPT Image 1.5 publishes a clear tier: 60 requests/minute by default, bursting to 120/minute, scaling to 1,000+ requests/minute on paid tiers. Nano Banana 2's Infer page doesn't publish an equivalent rate-limit number. Don't assume parity; check the model page directly before sizing a production job around it.
Which is cheaper at volume?
Nano Banana 2, marginally: $0.039/image versus GPT Image 1.5's $0.04/image flat. At 500 images that's a $0.50 difference, not the deciding factor. The real cost advantage is time: Nano Banana 2's roughly 1.4-second median latency against GPT Image 1.5's roughly 5 seconds means the same 500-image batch finishes about 3x faster.
Do either of these models add a visible watermark?
Nano Banana 2 embeds Google's SynthID, an invisible watermark, on every image (see Google DeepMind's SynthID overview). GPT Image 1.5's Infer page doesn't document a watermark; instead it lists OpenAI's content-safety layer (CSAM detection, public-figure restrictions, and NSFW filtering) running on every generation.
Sources