Voxtral-Mini 3B vs Whisper Large V3: Which One’s Faster?

Which speech-to-text model delivers faster and more accurate transcriptions Voxtral-Mini 3B or Whisper Large V3?

We put Voxtral-Mini 3B and Whisper Large V3 head-to-head to find out which speech-to-text model performs better in real-world tasks. Using the same audio clips, we compared latency (speed) and word error rate (accuracy) to help you choose the right model for use cases like transcribing calls, meetings, or voice messages.

As speech-to-text systems become smarter and more reliable, they’re transforming how we interact with technology from voice assistants to customer support tools. Many speech-to-text models now make it possible to handle calls, meetings, and recordings with higher efficiency. Read on to see how Voxtral and Whisper stack up and which one could power your next voice-enabled application.

What Is Voxtral-Mini 3B?

Voxtral-Mini 3B is a new AI model that listens to speech and turns it into clear written text. It was created by a company called Mistral and is designed to be fast, lightweight, and accurate. What makes it special is that it not only understands speech but can also follow instructions and generate better-quality responses. 

Even though it’s smaller in size compared to some big models, it performs surprisingly well. This makes it a strong option for apps that need quick and reliable speech-to-text conversion.

Usage Setup

Here’s how you can set up and use Voxtral-Mini 3B in a Python environment using vLLM.

Setting up Voxtral-Mini 3B is simple if you're using Python. The model is built to work well with vLLm, a fast and efficient backend for running large language models, especially with audio input.

Requirements

• A GPU with at least 9.5 GB of memory (recommended: A100 or similar)
• Python installed
• vLLM installed with audio support

Installation Steps

Use the following command to install vLLM along with audio support:

uv pip install -U "vllm[audio]" --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightly

Running Voxtral-Mini as a Server

Once installed, you can start serving the model using:

vllm serve mistralai/Voxtral-Mini-3B-2507 --tokenizer_mode mistral --config_format mistral --load_format mistral

This sets up the model so you can send audio to it and get transcriptions or even summaries and answers.

Usage Code

import gradio as gr
import time
from jiwer import wer
from mistral_common.audio import Audio
from mistral_common.protocol.instruct.messages import RawAudio
from mistral_common.protocol.transcription.request import TranscriptionRequest
from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:8000/v1"
)

def transcribe_with_latency_and_wer(audio_file, reference_text):
    start_time = time.time()

    # Load audio file (must be supported format like wav/flac/ogg)
    audio = Audio.from_file(audio_file, strict=False)
    raw_audio = RawAudio.from_audio(audio)

    # Transcribe
    model_id = client.models.list().data[0].id
    request = TranscriptionRequest(
        model=model_id,
        audio=raw_audio,
        language="en",
        temperature=0.0
    ).to_openai(exclude=("top_p", "seed"))

    response = client.audio.transcriptions.create(**request)

    end_time = time.time()
    latency = end_time - start_time
    hypothesis = response.text.strip()

    # Compute WER
    reference = reference_text.strip()
    error = wer(reference, hypothesis) if reference else "N/A"
    return f"""📝 Transcription:\n{hypothesis}
📜 Reference:\n{reference}
📊 Word Error Rate (WER): {error if error == "N/A" else f"{error*100:.2f}%"}
⏱️ Latency: {latency:.2f} seconds
"""
# Gradio interface with reference text input
gr.Interface(
    fn=transcribe_with_latency_and_wer,
    inputs=[
        gr.Audio(type="filepath", label="Upload Audio File (.wav, .flac)"),
        gr.Textbox(label="Reference Text (Ground Truth)", placeholder="Enter the expected text here...")
    ],
    outputs="text",
    title="🎙️ Voxtral-Mini Transcription + WER",
    description="Upload an audio file and (optionally) its ground truth to measure transcription quality using WER."
).launch()

What Is Whisper Large V3?

Whisper Large V3 is a speech-to-text model developed by OpenAI. It can understand many languages and accurately convert spoken words into written text, even in noisy environments. It's widely used for subtitles, voice notes, and meeting transcriptions.

Model Comparison: Voxtral-Mini 3B vs Whisper Large V3

To see which model performs better, I tested both on the same audio clips and compared them based on two key things:

• Latency: How fast the model gives back the text
• WER (Word Error Rate): How many mistakes it makes while transcribing

https://mistral.ai/news/voxtral

Speech Transcription:

https://mistral.ai/news/voxtral

Result Table

Suggested Reads- A Complete Guide to Using Whisper ASR: From Installation to Implementation

Conclusion:

In this comparison, Voxtral-Mini 3B stood out for its speed and accuracy, delivering faster transcriptions with fewer errors. Its advanced features, like summarizing audio and answering questions directly from voice input, make it even more versatile for real-world applications. 

Whisper Large V3, however, remains a solid contender, especially if you need robust multilingual support or work with audio in noisy environments. Choosing between them depends on your priorities. 

If you want quick, high-quality transcriptions and smart voice features, Voxtral-Mini is the clear winner. But for broader language coverage, Whisper still holds its ground.

Both are powerful tools, now it’s up to you to decide which fits your needs best.