Google: Nano Banana Pro (Gemini 3 Pro Image) vs Microsoft AI: MAI-Image-2.6
A side-by-side comparison of Google: Nano Banana Pro (Gemini 3 Pro Image) ($7.00 / 1M tokens) and Microsoft AI: MAI-Image-2.6 ($17.50 / 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) | Microsoft AI: MAI-Image-2.6 | |
|---|---|---|
| Provider | Microsoft | |
| Input | $7.00 / 1M tokens | $17.50 / 1M tokens |
| Output | $42.00 / 1M tokens | - |
| Cache read | $0.70 / 1M tokens | - |
| Cache write | $1.31 / 1M tokens | - |
| Reasoning | $42.00 / 1M tokens | - |
| Image output | $420.00 / 1M tokens | $133.00 / 1M tokens |
| Audio input | $7.00 / 1M tokens | - |
| Image | $0.000007 / image | - |
| Web search | $49.00 / 1K searches | - |
| Image input | - | $133.00 / 1M tokens |
| Context window | 131,072 | 4,096 |
| Max output | 32,768 | 1,024 |
| Input | image, text | text, image |
| Output | image, text | image |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Jun 18, 2026 | Sep 4, 2026 |
Key differences
- Google: Nano Banana Pro (Gemini 3 Pro Image) is 60% cheaper on input tokens.
- Google: Nano Banana Pro (Gemini 3 Pro Image) reads 32.0× more context (131,072 tokens).
- Only Google: Nano Banana Pro (Gemini 3 Pro Image) supports tool calling.
- Only Google: Nano Banana Pro (Gemini 3 Pro Image) supports structured JSON output.
- Only Google: Nano Banana Pro (Gemini 3 Pro Image) supports reasoning effort control.
In depth
Google: Nano Banana Pro (Gemini 3 Pro Image) is an image model from Google, listed on api14 under the model id google/gemini-3-pro-image. Microsoft AI: MAI-Image-2.6 is an image model from Microsoft, listed under the model id microsoft/mai-image-2.6. Both sit in the image category, so both are built for work where pictures are the point rather than an afterthought, but they come from different providers and were released at different times, with the Google model appearing on 2026-06-18 and the Microsoft model on 2026-09-04.
The clearest split is context and output. The Google model accepts image and text as input and can return image and text as output, while the Microsoft model accepts text and image as input but returns image only. That means anything where you want the model to talk back in words, explain a render, or hand you a caption alongside the picture, points at the Google model. Context is the other wide gap: the Google model carries 131072 tokens, the Microsoft model 4096. For long prompts, multi image references, or extended back and forth, the Google model has far more room. The parameter lists tell a similar story. The Google model supports tools, tool choice, structured outputs, reasoning, include reasoning, response format, seed, stop, temperature, top p and max tokens. The Microsoft model supports max completion tokens, max tokens and temperature. So tool calling, structured output and reasoning control are only on the Google side.
In practice, pick the Google model when the task needs more than a single image out. Editing pipelines that pass several reference images, agents that call tools mid generation, and anything that needs a structured response or a reasoning pass belong there. Pick the Microsoft model when you want a straightforward text prompt to image call and nothing else, since its narrower surface is simpler to wire up and it does not ask you to think about tool or reasoning parameters. If your prompt is short and your output is a picture, the Microsoft model is a reasonable default. If your prompt is long, or your output needs words or structure, the Google model is the one to reach for.
Calling either through api14 works the same way, because the API is OpenAI compatible. You point your client at the same base URL, send the same chat completions style request body, and change only the model id: google/gemini-3-pro-image for the Google model, microsoft/mai-image-2.6 for the Microsoft model. Everything else in your code, the client library, the auth header, the request shape, stays put. Just keep the parameter differences in mind, since the Microsoft model will not accept tools, structured outputs or reasoning fields, and the Google model will not accept max completion tokens.
There is one case where the Microsoft model is still the better pick even against the wider feature set: a short, single image generation where you do not need text back, tools, structured output or reasoning, and you would rather not carry the extra parameters. On price, the Google model is the cheaper of the two on input, while output pricing is not meaningfully different between them, so cost alone is not a strong reason to switch either way. Check the live price table on this page for the current numbers before you decide.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "google/gemini-3-pro-image" to "microsoft/mai-image-2.6" and prompts, streaming and tools keep working.
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": "microsoft/mai-image-2.6",
"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"));
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