Mistral: Voxtral Small 24B 2507 STT vs OpenAI: GPT Transcribe
A side-by-side comparison of Mistral: Voxtral Small 24B 2507 STT ($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 Small 24B 2507 STT | OpenAI: GPT Transcribe | |
|---|---|---|
| Provider | Mistral | 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 | Aug 13, 2026 | Aug 5, 2026 |
Key differences
- Mistral: Voxtral Small 24B 2507 STT is 33% cheaper on input tokens.
In depth
Mistral: Voxtral Small 24B 2507 STT is a transcription model from Mistral. OpenAI: GPT Transcribe is a transcription model from OpenAI. Both belong to the transcription category and both were released in August 2026, with GPT Transcribe arriving first and Voxtral Small following about a week later. Neither name suggests a general purpose chat model, so you should treat both as dedicated speech to text endpoints rather than assistants you can hand a conversation to.
The two models line up almost exactly on the technical surface. Both accept audio as their only input modality and both return transcription as their only output modality. Neither lists a context length, so no documented token window separates them. Neither lists any supported parameters, which means you do not get to tune sampling, temperature or similar knobs through the API for either one. There are no differences recorded for tool use, structured output or reasoning, so nothing suggests one can do something the other cannot in those areas. The one concrete difference is cost on the input side: Voxtral Small is the cheaper of the two for input. Output pricing is not distinguished, so treat that side as about the same.
For most transcription work, both models are interchangeable on paper, so the deciding factor is usually price and provider preference. If you are transcribing large volumes of audio and input cost dominates your bill, Voxtral Small is the natural default because it is the cheaper input option. If your team already standardizes on OpenAI models, or you want consistency with other OpenAI endpoints you call, GPT Transcribe keeps that stack uniform, and there is no listed capability gap that would make it the wrong choice. Because neither model exposes tunable parameters, you should not pick one expecting finer control over the output; plan to handle formatting, punctuation cleanup and downstream structuring in your own code regardless of which you call.
Calling either model through api14 works the same way. You send an audio input to a transcription style request using the OpenAI-compatible schema, and the only thing that changes between the two is the model id. Pass "mistralai/voxtral-small-24b-2507-stt" to route to Mistral's model, or pass "openai/gpt-transcribe" to route to OpenAI's. Your request body, headers and authentication stay as they are, so switching providers is a one line change and you can compare results on your own audio without rewriting integration code.
One caveat before you commit: the cheaper input side is the only recorded differentiator, and it is qualitative. If your workload is small or latency sensitive rather than cost sensitive, the price gap may not matter, and either model is a reasonable pick. Check the live price table on this page for current numbers, test both on a sample of your real audio, and then standardize on whichever gives you acceptable transcripts at the cost you are willing to pay.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "mistralai/voxtral-small-24b-2507-stt" 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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