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Muse Spark 1.3 vs GPT-5.4 Mini: API price, context & features · api14

Meta: Muse Spark 1.3 vs OpenAI: GPT-5.4 Mini

A side-by-side comparison of Meta: Muse Spark 1.3 ($4.38 / 1M tokens) and OpenAI: GPT-5.4 Mini ($2.63 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Meta: Muse Spark 1.3	OpenAI: GPT-5.4 Mini
Provider	Meta	OpenAI
Input	$4.38 / 1M tokens	$2.63 / 1M tokens
Output	$14.88 / 1M tokens	$15.75 / 1M tokens
Cache read	$0.52 / 1M tokens	$0.26 / 1M tokens
Web search	$8.75 / 1K searches	$35.00 / 1K searches
Context window	1,048,576	400,000
Max output	943,718	128,000
Input	text, image, video, file	file, image, text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Sep 2, 2026	Mar 17, 2026

Key differences

OpenAI: GPT-5.4 Mini is 40% cheaper on input tokens.
Meta: Muse Spark 1.3 is 6% cheaper on output tokens.
Meta: Muse Spark 1.3 reads 2.6× more context (1,048,576 tokens).
A request with 1,000 input and 500 output tokens costs $0.01 with Meta: Muse Spark 1.3 and $0.01 with OpenAI: GPT-5.4 Mini.

In depth

Meta: Muse Spark 1.3 is a text model from Meta, released on 2 September 2026. OpenAI: GPT-5.4 Mini is a text model from OpenAI, released on 17 March 2026. Both are served through api14 as chat models with text output, so from a developer's point of view the main question is not what they are but which one fits the job in front of you.

The clearest split is context and input handling. Muse Spark 1.3 accepts a context of 1,048,576 tokens, while GPT-5.4 Mini accepts 400,000, so Muse Spark 1.3 is the larger window by a wide margin. Muse Spark 1.3 also takes text, image, video and file inputs, whereas GPT-5.4 Mini takes text, image and file inputs but not video. On tool calling, structured outputs and reasoning, the two are not meaningfully different, so you can rely on those features in either. The parameter surfaces differ in small ways: Muse Spark 1.3 exposes temperature, top_p, top_k and repetition_penalty, while GPT-5.4 Mini exposes seed and verbosity instead, and it uses max_completion_tokens alongside max_tokens. If your code depends on sampling controls like temperature or top_k, that is a point in favour of Muse Spark 1.3; if it depends on a seed for repeatable runs, GPT-5.4 Mini is the one that offers it.

For day to day work, pick based on what you are feeding the model and how much of it. If you are summarising or querying very large documents, long transcripts, or whole repositories in a single call, Muse Spark 1.3 gives you far more room before you have to chunk, and its video input means you can pass video directly rather than extracting frames yourself. If your inputs are ordinary sized prompts, chat turns, screenshots and attached files, GPT-5.4 Mini is a reasonable default and its seed parameter helps when you want stable outputs across runs. Cost is worth checking on the live price table rather than guessing: GPT-5.4 Mini is the cheaper of the two on input, while Muse Spark 1.3 is the cheaper of the two on output. That means the cheaper choice depends on your ratio of input to output tokens, so a workload that reads a lot and writes little leans one way, and a workload that generates long responses leans the other.

Calling either model 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 request shape, and change only the model id: use "meta/muse-spark-1.3" for Muse Spark 1.3 and "openai/gpt-5.4-mini" for GPT-5.4 Mini. Your messages array, tool definitions, response_format and reasoning settings carry over unchanged. The one thing to watch is parameters: if you set temperature or top_k, that applies to Muse Spark 1.3, and if you set seed or verbosity, that applies to GPT-5.4 Mini, so guard those fields if you route the same code to both.

There are cases where the smaller window is still the better pick. If your task is short and output heavy, GPT-5.4 Mini is cheaper on input and you are not paying for context you will never fill, and its seed support makes it easier to reproduce a run exactly. Conversely, if you need video input or a million token window, Muse Spark 1.3 is the only one of the two that offers it, and its lower output cost helps on long generations.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "meta/muse-spark-1.3" to "openai/gpt-5.4-mini" and prompts, streaming and tools keep working.

Meta: Muse Spark 1.3 details & playground →OpenAI: GPT-5.4 Mini 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": "openai/gpt-5.4-mini",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Meta: Muse Spark 1.3 vs OpenAI: GPT-5.4 Mini

Specifications

Key differences

In depth

Switch with one string

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