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Nano Banana 2.1 vs Muse Image: API price, context & features · api14

Google: Nano Banana 2.1 vs Meta: Muse Image

A side-by-side comparison of Google: Nano Banana 2.1 ($5.25 / 1M tokens) and Meta: Muse Image ($8.38 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Google: Nano Banana 2.1	Meta: Muse Image
Provider	Google	Meta
Input	$5.25 / 1M tokens	-
Output	$26.25 / 1M tokens	-
Image output	$105.00 / 1M tokens	$8.38 / 1M tokens
Web search	$49.00 / 1K searches	-
Image input	-	$8.38 / 1M tokens
Context window	65,536	65,536
Max output	58,982	58,982
Input	image, text	text, image
Output	image, text	image
Tool calling
Structured output
Reasoning control
Added	Oct 6, 2026	Aug 26, 2026

Key differences

Only Google: Nano Banana 2.1 supports tool calling.
Only Google: Nano Banana 2.1 supports structured JSON output.
Only Google: Nano Banana 2.1 supports reasoning effort control.

In depth

Google: Nano Banana 2.1 is an image model from Google, and Meta: Muse Image is an image model from Meta. Both sit in the same category on api14, so you are choosing between two vendors' image generators rather than between an image model and a text model. Google released Nano Banana 2.1 on 2026-10-06, while Meta released Muse Image earlier, on 2026-08-26.

The two models match on context length: both accept 65536 tokens. They also match on input modalities, since both take text and image input. Where they diverge is on output. Nano Banana 2.1 can return both image and text, while Muse Image returns image only. The parameter lists tell a similar story. Nano Banana 2.1 supports tools, tool_choice, structured_outputs, response_format, reasoning, reasoning_effort and include_reasoning, and Muse Image supports none of those. Muse Image instead exposes repetition_penalty and top_k, which Nano Banana 2.1 does not list. Both share max_tokens, temperature and top_p, and Nano Banana 2.1 adds seed. So if your pipeline needs function calling, JSON-shaped output, or a reasoning pass alongside the image, only one of these two can do it.

For straightforward text-to-image or image-to-image generation where you just want pixels back, Muse Image is a reasonable default, and its sampling controls (top_k, repetition_penalty) give you knobs that some image workflows still rely on. Pick Nano Banana 2.1 when the image is part of a larger agent loop: you want the model to call tools, emit structured output you can parse, or produce a text answer next to the image. It is also the pick when you want reasoning controls exposed, since Muse Image offers no reasoning parameters at all. Neither model is listed as cheaper on input or output, and neither has a larger context window, so cost and context should not decide this for you; capability fit should.

Calling either one through api14 works the same way, because the API is OpenAI-compatible. You point your client at the same base URL, send the same request shape, and change only the model id string. Use "google/gemini-nano-banana-2.1" for the Google model and "meta/muse-image" for the Meta model. Everything else in your request (messages, temperature, max_tokens) stays put, though parameters outside a model's supported list will not apply to it.

There is a case where Muse Image remains the better choice even though Nano Banana 2.1 supports more. If your task is pure image generation and you specifically want top_k or repetition_penalty to steer sampling, those parameters exist only on Muse Image. In that situation the extra tool, structured output and reasoning support on Nano Banana 2.1 buys you nothing, and the simpler parameter surface is the one you actually need.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "google/gemini-nano-banana-2.1" to "meta/muse-image" and prompts, streaming and tools keep working.

Google: Nano Banana 2.1 details & playground →Meta: Muse 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": "meta/muse-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 2.1 vs Meta: Muse Image

Specifications

Key differences

In depth

Switch with one string

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