mistralai/Voxtral-Small-24B-2507

Voxtral Small 1.0 (24B) - 2507

Voxtral Small is an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding.

Learn more about Voxtral in our blog post here.

Key Features

Voxtral builds upon Mistral Small 3 with powerful audio understanding capabilities.

• Dedicated transcription mode: Voxtral can operate in a pure speech transcription mode to maximize performance. By default, Voxtral automatically predicts the source audio language and transcribes the text accordingly
• Long-form context: With a 32k token context length, Voxtral handles audios up to 30 minutes for transcription, or 40 minutes for understanding
• Built-in Q&A and summarization: Supports asking questions directly through audio. Analyze audio and generate structured summaries without the need for separate ASR and language models
• Natively multilingual: Automatic language detection and state-of-the-art performance in the world’s most widely used languages (English, Spanish, French, Portuguese, Hindi, German, Dutch, Italian)
• Function-calling straight from voice: Enables direct triggering of backend functions, workflows, or API calls based on spoken user intents
• Highly capable at text: Retains the text understanding capabilities of its language model backbone, Mistral Small 3.1

Benchmark Results

Audio

Average word error rate (WER) over the FLEURS, Mozilla Common Voice and Multilingual LibriSpeech benchmarks:

Text

Usage

The model can be used with the following frameworks;

• vllm (recommended): See here

Notes:

• temperature=0.2 and top_p=0.95 for chat completion (e.g. Audio Understanding) and temperature=0.0 for transcription
• Multiple audios per message and multiple user turns with audio are supported
• Function calling is supported
• System prompts are not yet supported

vLLM (recommended)

We recommend using this model with vLLM.

Installation

Make sure to install vllm from "main", we recommend using uv

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

Doing so should automatically install mistral_common >= 1.8.1.

To check:

python -c "import mistral_common; print(mistral_common.__version__)"

Offline

You can test that your vLLM setup works as expected by cloning the vLLM repo:

git clone https://github.com/vllm-project/vllm && cd vllm

and then running:

python examples/offline_inference/audio_language.py --num-audios 2 --model-type voxtral

Serve

We recommend that you use Voxtral-Small-24B-2507 in a server/client setting.

1. Spin up a server:

vllm serve mistralai/Voxtral-Small-24B-2507 --tokenizer_mode mistral --config_format mistral --load_format mistral --tensor-parallel-size 2 --tool-call-parser mistral --enable-auto-tool-choice

Note: Running Voxtral-Small-24B-2507 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.

2. To ping the client you can use a simple Python snippet. See the following examples.

Audio Instruct

Leverage the audio capabilities of Voxtral-Small-24B-2507 to chat.

Make sure that your client has mistral-common with audio installed:

pip install --upgrade mistral_common\[audio\]

Python snippet

from mistral_common.protocol.instruct.messages import TextChunk, AudioChunk, UserMessage, AssistantMessage, RawAudio
from mistral_common.audio import Audio
from huggingface_hub import hf_hub_download

from openai import OpenAI

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://<your-server-host>:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id

obama_file = hf_hub_download("patrickvonplaten/audio_samples", "obama.mp3", repo_type="dataset")
bcn_file = hf_hub_download("patrickvonplaten/audio_samples", "bcn_weather.mp3", repo_type="dataset")

def file_to_chunk(file: str) -> AudioChunk:
    audio = Audio.from_file(file, strict=False)
    return AudioChunk.from_audio(audio)

text_chunk = TextChunk(text="Which speaker is more inspiring? Why? How are they different from each other? Answer in French.")
user_msg = UserMessage(content=[file_to_chunk(obama_file), file_to_chunk(bcn_file), text_chunk]).to_openai()

print(30 * "=" + "USER 1" + 30 * "=")
print(text_chunk.text)
print("\n\n")

response = client.chat.completions.create(
    model=model,
    messages=[user_msg],
    temperature=0.2,
    top_p=0.95,
)
content = response.choices[0].message.content

print(30 * "=" + "BOT 1" + 30 * "=")
print(content)
print("\n\n")
# The model could give the following answer:
# ```L'orateur le plus inspirant est le président.
# Il est plus inspirant parce qu'il parle de ses expériences personnelles
# et de son optimisme pour l'avenir du pays.
# Il est différent de l'autre orateur car il ne parle pas de la météo,
# mais plutôt de ses interactions avec les gens et de son rôle en tant que président.```

messages = [
    user_msg,
    AssistantMessage(content=content).to_openai(),
    UserMessage(content="Ok, now please summarize the content of the first audio.").to_openai()
]
print(30 * "=" + "USER 2" + 30 * "=")
print(messages[-1]["content"])
print("\n\n")

response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=0.2,
    top_p=0.95,
)
content = response.choices[0].message.content
print(30 * "=" + "BOT 2" + 30 * "=")
print(content)

Transcription

Voxtral-Small-24B-2507 has powerful transcription capabilities!

Make sure that your client has mistral-common with audio installed:

pip install --upgrade mistral_common\[audio\]

Python snippet

from mistral_common.protocol.transcription.request import TranscriptionRequest
from mistral_common.protocol.instruct.messages import RawAudio
from mistral_common.audio import Audio
from huggingface_hub import hf_hub_download

from openai import OpenAI

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://<your-server-host>:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id

obama_file = hf_hub_download("patrickvonplaten/audio_samples", "obama.mp3", repo_type="dataset")
audio = Audio.from_file(obama_file, strict=False)

audio = RawAudio.from_audio(audio)
req = TranscriptionRequest(model=model, audio=audio, language="en", temperature=0.0).to_openai(exclude=("top_p", "seed"))

response = client.audio.transcriptions.create(**req)
print(response)

Function Calling

Voxtral has some experimental function calling support. You can try as shown below.

Make sure that your client has mistral-common with audio installed:

pip install --upgrade mistral_common\[audio\]

Python snippet

from mistral_common.protocol.instruct.messages import AudioChunk, UserMessage, TextChunk
from mistral_common.protocol.transcription.request import TranscriptionRequest
from mistral_common.protocol.instruct.tool_calls import Function, Tool

from mistral_common.audio import Audio
from huggingface_hub import hf_hub_download

from openai import OpenAI

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://<your-server-host>:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id

tool = Tool(
    function=Function(
        name="get_current_weather",
        description="Get the current weather",
        parameters={
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA",
                },
                "format": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"],
                    "description": "The temperature unit to use. Infer this from the user's location.",
                },
            },
            "required": ["location", "format"],
        },
    )
)
tools = [tool.to_openai()]


weather_like = hf_hub_download("patrickvonplaten/audio_samples", "fn_calling.wav", repo_type="dataset")

def file_to_chunk(file: str) -> AudioChunk:
    audio = Audio.from_file(file, strict=False)
    return AudioChunk.from_audio(audio)

audio_chunk = file_to_chunk(weather_like)

print(30 * "=" + "Transcription" + 30 * "=")
req = TranscriptionRequest(model=model, audio=audio_chunk.input_audio, language="en", temperature=0.0).to_openai(exclude=("top_p", "seed"))
response = client.audio.transcriptions.create(**req)
print(response.text) # How is the weather in Madrid at the moment?
print("\n")


print(30 * "=" + "Function calling" + 30 * "=")
audio_chunk = file_to_chunk(weather_like)
user_msg = UserMessage(content=[audio_chunk]).to_openai()
response = client.chat.completions.create(
    model=model,
    messages=[user_msg],
    temperature=0.2,
    top_p=0.95,
    tools=[tool.to_openai()]
)
print(30 * "=" + "BOT 1" + 30 * "=")
print(response.choices[0].message.tool_calls)
print("\n\n")