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Muse Spark 1.3 vs Mistral Large 4: API price, context & features · api14

Meta: Muse Spark 1.3 vs Mistral: Mistral Large 4

A side-by-side comparison of Meta: Muse Spark 1.3 ($4.38 / 1M tokens) and Mistral: Mistral Large 4 ($2.45 / 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	Mistral: Mistral Large 4
Provider	Meta	Mistral
Input	$4.38 / 1M tokens	$2.45 / 1M tokens
Output	$14.88 / 1M tokens	$7.35 / 1M tokens
Cache read	$0.52 / 1M tokens	$0.25 / 1M tokens
Web search	$8.75 / 1K searches	-
Context window	1,048,576	1,048,576
Max output	943,718	262,144
Input	text, image, video, file	text, image
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Sep 2, 2026	Oct 6, 2026

Key differences

Mistral: Mistral Large 4 is 44% cheaper on input tokens.
Mistral: Mistral Large 4 is 51% cheaper on output tokens.
A request with 1,000 input and 500 output tokens costs $0.01 with Meta: Muse Spark 1.3 and $0.00613 with Mistral: Mistral Large 4.

In depth

Meta: Muse Spark 1.3 is a text model from Meta, and Mistral: Mistral Large 4 is a text model from Mistral. Both are served on api14 as chat models with a single text output, so from the outside they look similar: you send a request, you get text back. The differences that matter for a developer are in what you can feed them, which sampling and control parameters they accept, and which one costs less to run.

The two models share the same context length of 1048576 tokens, so neither has an advantage on how much you can put in a single request. Where they diverge is input modality. Muse Spark 1.3 accepts text, image, video and file inputs, while Mistral Large 4 accepts text and image only. If your pipeline needs to hand a model a video or a document file directly, Muse Spark 1.3 is the one that can take it. Their parameter surfaces also differ. Muse Spark 1.3 exposes include_reasoning, reasoning, reasoning_effort, repetition_penalty, top_k and the usual sampling controls. Mistral Large 4 exposes reasoning controls as well, plus frequency_penalty, presence_penalty, seed, stop, logprobs and top_logprobs. So if you want deterministic runs via seed, or token level log probabilities for scoring and debugging, Mistral Large 4 gives you those and Muse Spark 1.3 does not. Both support tools, tool_choice, structured_outputs and response_format, so function calling and JSON shaped output are available on either.

For task selection, start from the input side. If your workload is text only, or text plus images, both models are viable and the deciding factors become cost and the extra controls. Mistral Large 4 is the cheaper of the two on both input and output, so for high volume text or image workloads where you do not need video or file inputs, it is the more economical default. Reach for Muse Spark 1.3 when the request itself contains video or a file that you would rather not convert into text first, since that is a capability Mistral Large 4 does not list. If you need logprobs or top_logprobs for ranking, confidence estimates or evaluation harnesses, or a seed for reproducible sampling, Mistral Large 4 is the better fit regardless of price.

Calling either model through api14 works the same way, because the gateway is OpenAI compatible. You point your existing OpenAI client at the api14 base URL, send your key, and set the model field. The only change between the two is that string: use meta/muse-spark-1.3 for Muse Spark 1.3 and mistralai/mistral-large-4-0 for Mistral Large 4. Everything else in the request body, messages, tools, response_format, max_tokens, stays where it is. That makes switching cheap to test: run the same prompt against both ids, compare the outputs on your own data, and keep whichever behaves better for the task.

There are still cases where the pricier model is the right call. If your input includes video or a file attachment, Mistral Large 4 cannot accept it, so Muse Spark 1.3 is the only option between the two. The same applies if you specifically want top_k or repetition_penalty in your sampling setup, since those appear on Muse Spark 1.3 and not on Mistral Large 4. Outside those cases, the cheaper model with the broader parameter set is usually the sensible starting point.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "meta/muse-spark-1.3" to "mistralai/mistral-large-4-0" and prompts, streaming and tools keep working.

Meta: Muse Spark 1.3 details & playground →Mistral: Mistral Large 4 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": "mistralai/mistral-large-4-0",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Meta: Muse Spark 1.3 vs Mistral: Mistral Large 4

Specifications

Key differences

In depth

Switch with one string

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