Meta: Muse Voice Transcribe 1.0 vs OpenAI: GPT Transcribe
A side-by-side comparison of Meta: Muse Voice Transcribe 1.0 ($0.01 / minute) and OpenAI: GPT Transcribe ($0.02 / minute) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.
Specifications
| Meta: Muse Voice Transcribe 1.0 | OpenAI: GPT Transcribe | |
|---|---|---|
| Provider | Meta | OpenAI |
| Audio | $0.01 / minute | $0.02 / minute |
| Context window | - | - |
| Max output | - | - |
| Input | audio | audio |
| Output | transcription | transcription |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Sep 11, 2026 | Aug 5, 2026 |
Key differences
- Meta: Muse Voice Transcribe 1.0 is 33% cheaper on input tokens.
In depth
Meta: Muse Voice Transcribe 1.0 is a transcription model from Meta, and OpenAI: GPT Transcribe is a transcription model from OpenAI. Both sit in the same category, so neither is a general chat or reasoning model that happens to accept audio. You send audio in and you get a transcription out. Meta's model was released on 11 September 2026, and OpenAI's on 5 August 2026, so both are recent additions to the transcription line up on api14.
On the published facts, the two models are close to identical in shape. Both accept audio as their only input modality and both return a transcription as their only output modality. Neither lists a context length, so there is no documented difference in how much audio either can take in one call. Neither lists supported parameters, so there is no documented difference in tuning knobs such as temperature or language hints. The qualitative comparison flags only one difference: input is cheaper on Meta: Muse Voice Transcribe 1.0. Output pricing is about the same on both sides, and there is no meaningful difference in context size, tool use, structured output or reasoning. In short, this is a choice between two models that do the same job, with a price edge on the Meta side for input.
For most transcription work, that price edge is the deciding factor. If your workload is dominated by audio going in, batch jobs, archives, call recordings, meeting capture, then Meta: Muse Voice Transcribe 1.0 is the cheaper call and there is nothing in the published capabilities that would push you the other way. Pick OpenAI: GPT Transcribe when you want to stay inside the OpenAI model family for consistency with the rest of your stack, or when your own evaluation on your audio shows it handles your accents, domain vocabulary or noisy recordings better. Since neither model exposes supported parameters, you cannot tune either one through the API, so the choice comes down to price and to how each performs on your data.
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 audio in the same request shape you would use against OpenAI, and set the model field. To use Meta's model, set the model id to "meta/muse-voice-transcribe-1.0". To use OpenAI's, set it to "openai/gpt-transcribe". Nothing else in the request changes. That makes it cheap to test both: run the same audio through each model id, compare the transcripts, and then commit to one. You can also switch per request if you want to route different jobs to different models.
The case for OpenAI: GPT Transcribe is mostly about fit rather than features. If your team already standardises on OpenAI models, keeping transcription on "openai/gpt-transcribe" avoids a second vendor relationship and keeps billing and logging in one place. It is also the sensible default if you have already validated its output quality on your audio, since the published facts show no capability gap that would justify moving. Check the live price table for current rates before you decide, and let your own transcript quality tests settle any remaining doubt.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "meta/muse-voice-transcribe-1.0" to "openai/gpt-transcribe" and prompts, streaming and tools keep working.
import fs from "node:fs";
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api14.com/api/v1",
apiKey: process.env.API14_API_KEY,
});
const result = await client.audio.transcriptions.create({
model: "openai/gpt-transcribe",
file: fs.createReadStream("audio.mp3"),
});
console.log(result.text);
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