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Gemma 4 26B A4B vs Muse Spark 1.3 Contributor: API price, context & features · api14

Google: Gemma 4 26B A4B vs Meta: Muse Spark 1.3 Contributor

A side-by-side comparison of Google: Gemma 4 26B A4B ($0.24 / 1M tokens) and Meta: Muse Spark 1.3 Contributor ($0.35 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Google: Gemma 4 26B A4B	Meta: Muse Spark 1.3 Contributor
Provider	Google	Meta
Input	$0.24 / 1M tokens	$0.35 / 1M tokens
Output	$0.79 / 1M tokens	$0.70 / 1M tokens
Cache read	$0.13 / 1M tokens	$0.00700 / 1M tokens
Web search	-	$8.75 / 1K searches
Context window	262,144	1,048,576
Max output	235,929	943,718
Input	image, text, video	text, image, video, file
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Apr 3, 2026	Sep 2, 2026

Key differences

Google: Gemma 4 26B A4B is 32% cheaper on input tokens.
Meta: Muse Spark 1.3 Contributor is 11% cheaper on output tokens.
Meta: Muse Spark 1.3 Contributor reads 4.0× more context (1,048,576 tokens).
A request with 1,000 input and 500 output tokens costs $0.000630 with Google: Gemma 4 26B A4B and $0.000700 with Meta: Muse Spark 1.3 Contributor.

In depth

Google: Gemma 4 26B A4B is a text model from Google, listed under the model id google/gemma-4-26b-a4b-it. Meta: Muse Spark 1.3 Contributor is a text model from Meta, listed under the model id meta/muse-spark-1.3-contributor. Both are served through api14 as chat models with text output, so the choice between them comes down to context size, input types, parameter support and price rather than to a difference in the kind of output they produce.

The clearest split is context length. Gemma 4 26B A4B accepts up to 262,144 tokens, while Muse Spark 1.3 Contributor accepts up to 1,048,576 tokens, so Meta's model is the one to reach for when a single request has to hold a very large amount of material. On input modalities, Gemma 4 26B A4B takes image, text and video, and Muse Spark 1.3 Contributor takes text, image, video and file, so Meta's model adds file input on top of the same image and video support. Neither model outputs anything other than text. The two also expose different sampling and control parameters: Gemma 4 26B A4B supports a broader set including frequency_penalty, logit_bias, logprobs, min_p, presence_penalty, seed, stop, top_logprobs and max_completion_tokens, while Muse Spark 1.3 Contributor supports a smaller set that includes reasoning_effort. If your code depends on logprobs, logit_bias, seeds or explicit penalty controls, Gemma 4 26B A4B is the safer target. On price, Gemma 4 26B A4B is the cheaper option for input and Muse Spark 1.3 Contributor is the cheaper option for output, so the cost balance shifts with how much you send versus how much you generate.

For everyday work where prompts and responses are moderate in size and mostly text, Gemma 4 26B A4B is a reasonable default, particularly if you lean on logprobs, seeds or penalty parameters, or if your workload is input heavy. Choose Muse Spark 1.3 Contributor when the request itself is enormous, such as a long document set, a large transcript or a big codebase, or when you need to pass a file as input alongside text, images or video. It is also the better pick when output volume dominates your bill, since its output side is the cheaper of the two. If you need reasoning_effort as a control, that parameter is listed for Meta's model and not for Google's, which may settle the decision on its own.

Calling either model through api14 works the same way. Both are OpenAI-compatible, so you point your existing client at the api14 base URL, send a chat completions request with your messages, and set the model field to google/gemma-4-26b-a4b-it or meta/muse-spark-1.3-contributor. Switching between them is a one line change to that model id; the request shape, authentication and response handling stay as they are. Check the live price table on the page before you commit, since the numbers there are current and this text is not.

There are cases where the other model still wins. If you are already set up around Gemma 4 26B A4B's parameter set and your inputs are small, there is little reason to move to Meta's model just for context you will not use. Conversely, if your task is dominated by very long inputs or file attachments, Muse Spark 1.3 Contributor is the one to call even if you would otherwise prefer Google's parameter coverage.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "google/gemma-4-26b-a4b-it" to "meta/muse-spark-1.3-contributor" and prompts, streaming and tools keep working.

Google: Gemma 4 26B A4B details & playground →Meta: Muse Spark 1.3 Contributor 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.chat.completions.create({
  "model": "meta/muse-spark-1.3-contributor",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Google: Gemma 4 26B A4B vs Meta: Muse Spark 1.3 Contributor

Specifications

Key differences

In depth

Switch with one string

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