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Muse Wonder vs GPT-5.6 Sol Pro: API price, context & features · api14

Meta: Muse Wonder vs OpenAI: GPT-5.6 Sol Pro

A side-by-side comparison of Meta: Muse Wonder ($14.00 / 1M tokens) and OpenAI: GPT-5.6 Sol Pro ($14.00 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Meta: Muse Wonder	OpenAI: GPT-5.6 Sol Pro
Provider	Meta	OpenAI
Input	$14.00 / 1M tokens	$14.00 / 1M tokens
Output	$70.00 / 1M tokens	$70.00 / 1M tokens
Cache read	$0.70 / 1M tokens	$1.40 / 1M tokens
Web search	$8.75 / 1K searches	$35.00 / 1K searches
Cache write	-	$17.50 / 1M tokens
Context window	1,048,576	1,050,000
Max output	943,718	128,000
Input	text, image, video, file, audio	file, image, text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Oct 12, 2026	Jul 9, 2026

Key differences

Both have about the same input price.
A request with 1,000 input and 500 output tokens costs $0.05 with Meta: Muse Wonder and $0.05 with OpenAI: GPT-5.6 Sol Pro.

In depth

Meta: Muse Wonder is a text model from Meta, and OpenAI: GPT-5.6 Sol Pro is a text model from OpenAI. Both are served through api14 under the model ids meta/muse-wonder and openai/gpt-5.6-sol-pro. They sit in the same category, so the choice between them comes down to modality coverage, parameter support and which provider's behaviour you prefer, not to one being a different class of tool.

The clearest difference is input. Muse Wonder accepts text, image, video, file and audio input, while GPT-5.6 Sol Pro accepts file, image and text. If your pipeline feeds in audio or video, Muse Wonder is the one that can take it directly. Both produce text only, so neither returns images or audio. Their context windows are close enough to be treated as equivalent for planning purposes, around a million tokens each, which is enough for very large documents, long transcripts or big codebases in a single call. On parameters, Muse Wonder exposes temperature, top_p, top_k and repetition_penalty alongside reasoning controls, structured outputs and tools. GPT-5.6 Sol Pro exposes seed and verbosity instead, plus reasoning controls, structured outputs and tools. If you rely on sampling controls like top_k or repetition_penalty, Muse Wonder is the natural fit; if you want a seed for reproducibility or a verbosity dial, GPT-5.6 Sol Pro has those. Pricing is about the same on both input and output, so cost should not be the deciding factor here.

For most tasks either model will do the job, so pick on the edges. Reach for Muse Wonder when the input is not plain text, for example a recording to transcribe and summarise, a screen capture to reason over, or a mixed bundle of files and media in one request. It is also the better choice when you want fine control over sampling through top_k or repetition_penalty, or when you are porting prompts that already depend on those knobs. Reach for GPT-5.6 Sol Pro when your inputs are text, images and files only, and when you want a seed to make runs repeatable or a verbosity setting to shape how much the model says. Workloads that lean on OpenAI-style reasoning effort and completion token limits fit naturally there too.

Calling either model through api14 works the same way, because the API is OpenAI-compatible. You point your client at the api14 base URL, send your key, and set the model field. Switching between them is a one line change: use meta/muse-wonder for one and openai/gpt-5.6-sol-pro for the other. The request and response shapes stay the same, so tools, structured outputs and reasoning options carry over, with the caveat that a parameter only supported by one model (top_k on Muse Wonder, seed or verbosity on GPT-5.6 Sol Pro) should be dropped when you switch to the other. Nothing else in your integration needs to change.

There are still cases where the model you did not pick first is the better call. If you started with Muse Wonder for its audio and video input but your task is plain text with images and files, GPT-5.6 Sol Pro covers it and gives you seed and verbosity control. If you started with GPT-5.6 Sol Pro but your inputs include audio or video, or your prompts depend on top_k or repetition_penalty, Muse Wonder is the one that supports them. Since pricing is about the same on both sides, that decision can be made purely on capability fit.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "meta/muse-wonder" to "openai/gpt-5.6-sol-pro" and prompts, streaming and tools keep working.

Meta: Muse Wonder details & playground →OpenAI: GPT-5.6 Sol Pro 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-5.6-sol-pro",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Meta: Muse Wonder vs OpenAI: GPT-5.6 Sol Pro

Specifications

Key differences

In depth

Switch with one string

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