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Muse Spark 1.3 vs Muse Spark Open: API price, context & features · api14

Meta: Muse Spark 1.3 vs Meta: Muse Spark Open

A side-by-side comparison of Meta: Muse Spark 1.3 ($4.38 / 1M tokens) and Meta: Muse Spark Open ($4.38 / 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	Meta: Muse Spark Open
Provider	Meta	Meta
Input	$4.38 / 1M tokens	$4.38 / 1M tokens
Output	$14.88 / 1M tokens	$14.88 / 1M tokens
Cache read	$0.52 / 1M tokens	$0.52 / 1M tokens
Web search	$8.75 / 1K searches	-
Context window	1,048,576	1,048,576
Max output	943,718	943,718
Input	text, image, video, file	text, image, video, file, audio
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Sep 2, 2026	Oct 15, 2026

Key differences

Both have about the same input price.
A request with 1,000 input and 500 output tokens costs $0.01 with Meta: Muse Spark 1.3 and $0.01 with Meta: Muse Spark Open.

In depth

Meta: Muse Spark 1.3 and Meta: Muse Spark Open are both text generation models from Meta, served side by side on api14. They share the same model family and the same provider, so the choice between them is not about vendor trust or ecosystem, it is about which variant fits the call you are making. Muse Spark 1.3 is the earlier of the two, released on 2026-09-02, while Muse Spark Open arrived later, on 2026-10-15.

The two models are closer than their names suggest. Both accept text, image, video and file input and both return text only, and both expose a context window of 1,048,576 tokens, so there is no meaningful difference in how much you can feed them or how far back they can look. The real split is in input modalities and tuning surface. Muse Spark Open adds audio input on top of the shared set, which makes it the only one of the two that can take a spoken clip directly. Muse Spark Open also supports a wider set of sampling and decoding parameters, including frequency_penalty, presence_penalty, logit_bias, logprobs, top_logprobs, min_p, seed and stop, while Muse Spark 1.3 offers a narrower list centred on reasoning, tools, structured outputs and the usual temperature and top_p controls. If you need deterministic runs via seed, token level log probabilities, or fine grained penalty tuning, that is Muse Spark Open territory. If you need audio in, again Muse Spark Open.

For most day to day work the two are interchangeable. Long document analysis, code review over a large repository, multi turn tool calling, structured JSON output and reasoning heavy prompts are all supported by both, and both take the same 1M token context. Reach for Muse Spark 1.3 when your pipeline is already tuned against it and you do not need audio, logprobs or seed control, since switching buys you nothing on those tasks. Reach for Muse Spark Open when the input includes audio, when you want reproducible sampling through seed, when you are debugging with logprobs, or when you want the extra penalty and min_p knobs to shape output. The pricing picture is a tie on both input and output, so cost is not a deciding factor here; the live price table on this page has the current numbers.

Calling either model through api14 works the same way, because the gateway speaks the OpenAI compatible chat completions shape. You point your client at the api14 base URL, send your usual messages array, and set the model field to either meta/muse-spark-1.3 or meta/muse-spark-open. Everything else, your API key, headers, streaming flags, tool definitions and response_format, stays as it is. Moving a workload from one to the other is a one line change to the model id, which makes it cheap to A/B the two on your own traffic before committing.

There is one case where the earlier model is still the better pick: if your existing prompts, evals and guardrails were built and validated against Muse Spark 1.3, and you have no need for audio input, seed, logprobs or the extra penalties, there is no capability gap pushing you to migrate. Staying on Muse Spark 1.3 keeps your behaviour stable and avoids revalidating a working pipeline for a change that would not show up in your outputs.

Switch with one string

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

Meta: Muse Spark 1.3 details & playground →Meta: Muse Spark Open 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-open",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Meta: Muse Spark 1.3 vs Meta: Muse Spark Open

Specifications

Key differences

In depth

Switch with one string

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