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Nano Banana Pro (Gemini 3 Pro Image) vs GPT-5 Image: API price, context & features · api14

Google: Nano Banana Pro (Gemini 3 Pro Image) vs OpenAI: GPT-5 Image

A side-by-side comparison of Google: Nano Banana Pro (Gemini 3 Pro Image) ($7.00 / 1M tokens) and OpenAI: GPT-5 Image ($35.00 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Google: Nano Banana Pro (Gemini 3 Pro Image)	OpenAI: GPT-5 Image
Provider	Google	OpenAI
Input	$7.00 / 1M tokens	$35.00 / 1M tokens
Output	$42.00 / 1M tokens	$35.00 / 1M tokens
Cache read	$0.70 / 1M tokens	$4.38 / 1M tokens
Cache write	$1.31 / 1M tokens	-
Reasoning	$42.00 / 1M tokens	-
Image output	$420.00 / 1M tokens	$140.00 / 1M tokens
Audio input	$7.00 / 1M tokens	-
Image	$0.000007 / image	-
Web search	$49.00 / 1K searches	$35.00 / 1K searches
Context window	131,072	400,000
Max output	32,768	128,000
Input	image, text	image, text, file
Output	image, text	image, text
Tool calling
Structured output
Reasoning control
Added	Jun 18, 2026	Oct 14, 2025

Key differences

Google: Nano Banana Pro (Gemini 3 Pro Image) is 80% cheaper on input tokens.
OpenAI: GPT-5 Image is 17% cheaper on output tokens.
OpenAI: GPT-5 Image reads 3.1× more context (400,000 tokens).
Only Google: Nano Banana Pro (Gemini 3 Pro Image) supports tool calling.

In depth

Google: Nano Banana Pro (Gemini 3 Pro Image) is an image model from Google, listed under the model id google/gemini-3-pro-image. OpenAI: GPT-5 Image is an image model from OpenAI, listed under the model id openai/gpt-5-image. Both sit in the image category, and both accept image and text as input and can return image and text as output, so at a glance they are aimed at the same kind of work: generating and editing images alongside text in a single call.

The clearest split is context. GPT-5 Image carries a 400000 token context window, while Nano Banana Pro carries 131072 tokens, so the OpenAI model gives you roughly three times the room to work with. GPT-5 Image also accepts file input in addition to image and text, which Nano Banana Pro does not list. On parameters, Nano Banana Pro is the only one of the two that supports tools and tool_choice, so if your workflow needs the model to call functions, that capability lives on the Google side. The two are otherwise close on the shared surface: both support reasoning and include_reasoning, structured_outputs, response_format, seed, temperature, top_p, max_tokens and stop. GPT-5 Image adds logprobs, top_logprobs and verbosity, which Nano Banana Pro does not list. On price, Nano Banana Pro is the cheaper of the two for input, while GPT-5 Image is the cheaper of the two for output, so the better deal depends on whether your workload is input heavy or output heavy.

For practical selection, reach for GPT-5 Image when your task involves long inputs, many reference images, or attached files, since the larger context and file modality are what let you keep everything in one request instead of chunking it yourself. It is also the natural pick when most of your cost lands on generated output, because its output pricing is the lower of the two. Reach for Nano Banana Pro when you need tool calling, since it is the only one of the pair that supports tools and tool_choice, and when your cost is dominated by input tokens, where it is cheaper. If your task is a straightforward image plus text request with no tools and a modest prompt, either model can serve it, and the deciding factor is simply which side of the input and output cost split your usage falls on.

Calling either model through api14 works the same way, because the API is OpenAI compatible. You point your existing client at the api14 base URL, send your request in the usual chat completions shape with your image and text content, and set the model field to google/gemini-3-pro-image or openai/gpt-5-image. Switching between them is a one line change to that model id; the request and response structure stay the same. Keep in mind that the parameter sets are not identical, so if you rely on tools or tool_choice you are on the Google model, and if you rely on logprobs, top_logprobs or verbosity you are on the OpenAI model. The live price table on this page shows the current rates for both.

There is one case where the other model is still the better pick even if you started on the first. If you begin with Nano Banana Pro for its cheaper input and tool support, but your prompts grow into long documents or you need to attach files, GPT-5 Image becomes the better choice because of its larger context window and file input. Conversely, if you begin with GPT-5 Image for its context and cheaper output but later need the model to call tools, Nano Banana Pro is the one that supports tools and tool_choice.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "google/gemini-3-pro-image" to "openai/gpt-5-image" and prompts, streaming and tools keep working.

Google: Nano Banana Pro (Gemini 3 Pro Image) details & playground →OpenAI: GPT-5 Image details & playground →

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api14.com/api/v1",
  apiKey: process.env.API14_API_KEY,
});

const result = await client.images.generate({
  "model": "openai/gpt-5-image",
  "prompt": "A minimalist product photo of a ceramic mug on a wooden table",
  "n": 1,
  "aspect_ratio": "1:1"
});
import { writeFile } from "node:fs/promises";
await writeFile("image.png", Buffer.from(result.data[0].b64_json, "base64"));

Google: Nano Banana Pro (Gemini 3 Pro Image) vs OpenAI: GPT-5 Image

Specifications

Key differences

In depth

Switch with one string

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