Mistral: Voxtral Mini Transcribe 3 vs OpenAI: GPT Transcribe
A side-by-side comparison of Mistral: Voxtral Mini Transcribe 3 ($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
| Mistral: Voxtral Mini Transcribe 3 | OpenAI: GPT Transcribe | |
|---|---|---|
| Provider | Mistral | OpenAI |
| Audio | $0.01 / minute | $0.02 / minute |
| Context window | 65,536 | - |
| Max output | 52,428 | - |
| Input | audio | audio |
| Output | transcription | transcription |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Oct 20, 2026 | Aug 5, 2026 |
Key differences
- Mistral: Voxtral Mini Transcribe 3 is 22% cheaper on input tokens.
In depth
Mistral: Voxtral Mini Transcribe 3 is a transcription model from Mistral, 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 call one or the other when the job is turning speech into text, and the choice comes down to provider, context, and price rather than to a broad feature gap.
On capabilities the two are close. Both take audio as input and return a transcription as output, and neither exposes any supported parameters, so there is no sampling or tuning surface to compare. The one concrete difference in the data is context: Voxtral Mini Transcribe 3 lists a context length of 65536 tokens, while GPT Transcribe has no context length listed. That does not prove Voxtral handles longer audio in practice, only that its window is documented and the other is not. The qualitative comparison marks input pricing as cheaper on the Mistral side, with output pricing about the same or unknown, and no model is flagged as uniquely offering tools, structured output, or reasoning. Treat them as two implementations of the same task.
For picking between them, start with the input side of your bill. If you are transcribing a lot of audio and cost per input token matters, Voxtral Mini Transcribe 3 is the cheaper input option, and that advantage grows with volume. If you already standardise on OpenAI across your stack, or you want to keep transcription next to other OpenAI calls for operational reasons, GPT Transcribe is a reasonable default and the price difference on input may not be decisive at small scale. Where you have very long recordings and want a documented token budget to plan against, the listed 65536 token context on the Mistral model gives you something concrete to size against, while the OpenAI model gives you nothing to plan with.
Calling either one through api14 works the same way, because the API is OpenAI-compatible. You send your audio to the transcription endpoint with the same request shape, and the only thing that changes is the model id. Use "mistralai/voxtral-mini-transcribe-3-0" to route to the Mistral model, or "openai/gpt-transcribe" to route to the OpenAI model. Your client code, authentication, and response handling stay as they are, so switching providers is a one-line change and you can compare real output on your own audio before committing.
There is no case in the facts given where one model is clearly the better pick on capability alone, since both cover the same input and output modalities and neither adds tools, structured output, or reasoning. The honest position is that GPT Transcribe remains a fine choice when you prefer the OpenAI provider or when input price is not a concern, and Voxtral Mini Transcribe 3 is the better choice when cheaper input pricing or a documented context window matters to you.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "mistralai/voxtral-mini-transcribe-3-0" 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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