ElevenLabs: Scribe v2 Medical vs Google: Gemini 3.5 Transcribe
A side-by-side comparison of ElevenLabs: Scribe v2 Medical ($0.01 / minute) and Google: Gemini 3.5 Transcribe ($0.000420 / minute) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.
Specifications
| ElevenLabs: Scribe v2 Medical | Google: Gemini 3.5 Transcribe | |
|---|---|---|
| Provider | ElevenLabs | |
| Audio | $0.01 / minute | $0.000420 / minute |
| Output | - | $42.00 / 1M tokens |
| Context window | - | 98,304 |
| Max output | - | 32,768 |
| Input | audio | audio |
| Output | transcription | transcription |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Oct 7, 2026 | Sep 25, 2026 |
Key differences
- Google: Gemini 3.5 Transcribe is 97% cheaper on input tokens.
In depth
ElevenLabs: Scribe v2 Medical is a transcription model from ElevenLabs, released on 2026-10-07 and identified on api14 as elevenlabs/scribe-v2-medical. Google: Gemini 3.5 Transcribe is a transcription model from Google, released on 2026-09-25 and identified as google/gemini-3.5-transcribe. Both sit in the transcription category, so they are aimed at the same broad job: taking audio in and returning a transcript.
The two models are close in shape. Both accept audio as input and produce transcription as output, and neither exposes any supported parameters, so you cannot tune sampling or other generation settings through the request body. The listed difference is context: Gemini 3.5 Transcribe has a context length of 98304 tokens, while Scribe v2 Medical has no context length listed. On price, Gemini 3.5 Transcribe is the cheaper of the two on input. Output pricing is not differentiated in the data, and there is no signal that either model is the only one offering tools, structured output, or reasoning, so those are not deciding factors here.
For most transcription work, the practical choice comes down to two things: price and audio length. If you are transcribing large volumes of audio and input cost matters, Gemini 3.5 Transcribe is the cheaper input option, which makes it the natural default for batch jobs, long recordings, and anything where you are paying per unit of audio at scale. Scribe v2 Medical is the more specialized name, and its medical framing suggests it is intended for clinical or medical dictation style audio, so reach for it when the content is medical in nature and you want a model built with that domain in mind. If your audio is short and routine, either model will do the job, and the price difference is the simplest tiebreaker.
Calling either model through api14 works the same way because the API is OpenAI compatible. You send your audio to the transcription endpoint with the usual request shape, and the only thing that changes between the two is the model id. Use elevenlabs/scribe-v2-medical when you want the ElevenLabs medical transcription model, and google/gemini-3.5-transcribe when you want the Google model. Everything else in your client code, your authentication, and your request handling stays as it is, so switching between them is a one line change and you can compare results on your own audio without rewriting your integration.
There is also a case for keeping Scribe v2 Medical in the mix even when price points you toward Gemini 3.5 Transcribe. Because the ElevenLabs model is positioned specifically for medical transcription, audio that is clinical or medically specialized is where its intended strength lies, and that is the situation in which the cheaper input price of the Google model is least likely to be the deciding factor. For general purpose or high volume transcription, Gemini 3.5 Transcribe is the sensible default given its lower input cost and its listed context length.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "elevenlabs/scribe-v2-medical" to "google/gemini-3.5-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: "google/gemini-3.5-transcribe",
file: fs.createReadStream("audio.mp3"),
});
console.log(result.text);
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