Microsoft AI: MAI-Transcribe 2 vs OpenAI: GPT Transcribe
A side-by-side comparison of Microsoft AI: MAI-Transcribe 2 ($21.00 / 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
| Microsoft AI: MAI-Transcribe 2 | OpenAI: GPT Transcribe | |
|---|---|---|
| Provider | Microsoft | OpenAI |
| Audio | $21.00 / minute | $0.02 / minute |
| Context window | - | - |
| Max output | - | - |
| Input | audio | audio |
| Output | transcription | transcription |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Sep 3, 2026 | Aug 5, 2026 |
Key differences
- OpenAI: GPT Transcribe is 100% cheaper on input tokens.
In depth
Microsoft AI: MAI-Transcribe 2 is a transcription model from Microsoft, listed under the model id microsoft/mai-transcribe-2. OpenAI: GPT Transcribe is a transcription model from OpenAI, listed under the model id openai/gpt-transcribe. Both sit in the same category on api14, so you are choosing between two vendors offering the same basic job rather than between two different classes of tool. The Microsoft model carries a provider release date of 2026-09-03, and the OpenAI model carries a provider release date of 2026-08-05.
On the facts available, the two models are close to identical in shape. Both accept audio as input and return a transcription as output. Neither lists a context length, so there is no documented difference in how much audio either can handle in one call. Neither lists supported parameters, so there is no documented difference in the knobs you can turn at request time. The qualitative comparison marks no model as the only one with tool use, structured output or reasoning, and it marks no winner on context size. The one clear difference is cost on the input side, where the OpenAI model is the cheaper of the two. Output pricing is not distinguished, so treat the two as about the same there.
For practical selection, the input price difference is the main lever. If your workload is dominated by long audio and you pay per unit of input, openai/gpt-transcribe is the more economical default, and there is nothing in the listed capabilities that argues against it. If you are already standardized on Microsoft tooling, or you want to keep transcription traffic with the same vendor as the rest of your stack, microsoft/mai-transcribe-2 is a reasonable pick and the cost gap is a per-call line item rather than a functional limitation. Because neither model exposes supported parameters, do not plan around tuning either one through the API. Send clean audio and take the transcript.
Calling either model through api14 works the same way, because the gateway exposes an OpenAI-compatible API. You point your client at the same base URL, send the same request body shape with the audio payload, and change only the model id string. Use microsoft/mai-transcribe-2 for the Microsoft model and openai/gpt-transcribe for the OpenAI model. That means you can switch vendors by editing one field, or route a share of traffic to each and compare real transcripts on your own audio without rewriting your integration.
There is a case for the Microsoft model even when price favors OpenAI. If your organization has an existing agreement, billing relationship or data handling arrangement with Microsoft, keeping transcription on microsoft/mai-transcribe-2 avoids introducing a second vendor for a single capability. The listed facts do not show any capability advantage for either side, so that decision rests on procurement and operational fit rather than on model behavior.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "microsoft/mai-transcribe-2" 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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