Gemini Nano Banana 2.1 vs GPT Image 2
Which one, when
Both charge $30 per million image output tokens, so the difference is on input and in what each accepts. gemini-nano-banana-2.1 costs $1.5 per million input tokens against $5 for gpt-image-2, takes video as well as text and image, accepts up to 14 reference images, and can return text alongside the image. gpt-image-2 offers editing with mask inpainting and free-form resolutions with edges up to 3840 pixels, where gemini-nano-banana-2.1 offers 1K, 2K and 4K at fixed aspect ratios. Pick gemini-nano-banana-2.1 for reference-heavy or multi-turn work, and gpt-image-2 for masked edits and custom sizes.
Pricing
| Gemini Nano Banana 2.1 | GPT Image 2 | Δ | |
|---|---|---|---|
| Input / 1M tokens | $1.5 | $5 | 0.3× |
| Output / 1M tokens | $30 | $30 | = |
Rates from the live catalogue at build time; each model page carries the current rate card.
Where they sit · output price per 1M tokens across all 11 image-generation models on this billing unit (log scale)
Specs
| Gemini Nano Banana 2.1 | GPT Image 2 | |
|---|---|---|
| Input modalities | text image video | text image |
| Output modalities | text image | image |
| Released | 2026-10-06 | 2026-04-21 |
| Output sizes |
|
|
| Input modes | text-to-image, single-turn and multi-turn editing, up to 14 reference images | text-to-image, image edit with mask inpainting |
| Images per request | - | 10 |
| Formats | - | png, jpeg, webp |
| Notes | 1K, 2K and 4K output at fifteen aspect ratios (1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9, 9:21, 1:4, 4:1, 1:8, 8:1) up to 14 reference images per request single-turn editing and multi-turn conversational iteration | Flexible resolutions: edges up to 3840px in multiples of 16, ratio <=3:1, ~0.65-8.3MP total (incl. 4K 3840x2160) editing with mask inpainting all image inputs processed at high fidelity significantly improved text rendering (precise placement can still struggle) |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: Gemini Nano Banana 2.1 · GPT Image 2
One prompt, both models, measured through the gateway
One prompt, one request per model, no retries and no cherry-picking. This is the first result each model returned. Neither size nor duration was pinned: each model used its own default, because a request shaped to fit all of them would flatter none. Files here are re-encoded for the web, so judge composition and prompt adherence, not compression.
Switch between them with one line
Both ids are in every tab below; the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.images.generate(
model="gemini-nano-banana-2.1",
# model="gpt-image-2", # uncomment this line, comment the one above
prompt="a watercolor lighthouse at dawn",
size="1024x1024",
)
print(resp.data[0].b64_json[:80])import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.images.generate({
model: "gemini-nano-banana-2.1",
// model: "gpt-image-2", // uncomment this line, comment the one above
prompt: "a watercolor lighthouse at dawn",
size: "1024x1024",
});
console.log(resp.data?.[0]?.b64_json?.slice(0, 80));curl https://synthorai.io/v1/images/generations \
-H "Authorization: Bearer sk-syn-..." \
-H "Content-Type: application/json" \
-d '{
"model": "gemini-nano-banana-2.1",
# "model": "gpt-image-2", # uncomment this line, comment the one above
"prompt": "a watercolor lighthouse at dawn",
"size": "1024x1024"
}'package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
resp, _ := client.Images.Generate(context.TODO(), openai.ImageGenerateParams{
Model: "gemini-nano-banana-2.1",
// Model: "gpt-image-2", // uncomment this line, comment the one above
Prompt: "a watercolor lighthouse at dawn",
Size: "1024x1024",
})
fmt.Println(resp.Data[0].B64JSON[:80])
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.images.*;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ImagesResponse resp = client.images().generate(
ImageGenerateParams.builder()
.model("gemini-nano-banana-2.1")
// .model("gpt-image-2") // uncomment this line, comment the one above
.prompt("a watercolor lighthouse at dawn")
.size("1024x1024")
.build());
System.out.println(resp.data().orElseThrow().get(0).b64Json().orElseThrow().substring(0, 80));FAQ
Which is cheaper, Gemini Nano Banana 2.1 or GPT Image 2?
Gemini Nano Banana 2.1 is cheaper on the "Input / 1M tokens" row ($1.5 vs $5, 3.3× apart). Other rows may point the other way; the table above carries the full rate card, and real cost depends on your mix.
Can I A/B test Gemini Nano Banana 2.1 against GPT Image 2 without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key. Switching is a one-line change to the model id, so you can route a fraction of traffic to each and compare bills directly.
Does the price change with image size?
It depends on how the model bills. Per-image models charge the same regardless of prompt or output size; token-billed models scale with the resolution you render, so a 4K image costs a multiple of a small one. The table above shows which applies to each.