Mistral AI 发布 Voxtral 3 语音模型家族及基于 LiveKit 的 Voice Agent SDK

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Home Editors Pick Agentic AIMistral AI Releases Voxtral 3: Speech-to-Text, TTS and a Voice Agent SDK

Mistral AI Releases Voxtral 3: Speech-to-Text, TTS and a Voice Agent SDK

Asif Razzaq

October 20, 2026

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Mistral AI just released Voxtral 3. Mistral Voxtral 3 is a speech model family paired with a voice agent SDK. It ships 3 transcription models, 1 text-to-speech model and an SDK built on LiveKit. One place for listening, reasoning, tool calls and speaking. All 4 models are in public preview on the Mistral API.

TL;DR

Size:Voxtral Mini Transcribe 3 has 4B parameters. Small Transcribe 3 size: not disclosed. Both batch models list a 32k context.
Runs on: Mistral API (EU, US and global endpoints) or your own infrastructure via Mistral sales. GPU and VRAM needs: not disclosed. Mistral says Mini suits resource-constrained environments.
Performance: Top 3 on independent speech-to-text rankings, strongest on diarization and multilingual batch transcription.
Best: 2.1% AA-WER for Small Transcribe 3, #3 on Artificial Analysis.
Worst: Realtime at 240 ms: 7.4% WER on FLEURS 35, vs 4.2% for Gemini 3.5 Realtime.

What is in the Voxtral 3 family?

Mistral lists 4 models and 1 SDK:

Voxtral Small Transcribe 3: flagship batch transcription and diarization. Docs also list translation, speech Q&A, summarization and function calling.
Voxtral Mini Transcribe 3: a 4B model for cheaper, faster batch jobs.
Voxtral Mini Transcribe Realtime 3: streaming ASR over WebSocket with end-of-turn detection.
Voxtral Speech 3: text-to-speech, also called Voxtral Mini TTS 3 in the post.

All transcription models support diarization and context biasing. Context biasing takes key terms or free text to improve recall. Batch transcription covers 50 languages, including 24 official EU languages.

How does the Voxtral voice agent SDK work?

The SDK is built on LiveKit and runs 3 stages as 1 streaming pipeline. Realtime Transcribe 3 listens and detects when the caller stops speaking. A general LLM, such as Mistral Small 4, reasons and calls tools. Voxtral Speech 3 replies in the tone of the conversation so far.

Each turn completes in under 1 second. The agent stops talking when the caller cuts in. It handles phone calls over SIP and browser or app sessions over WebRTC. Because each stage is a separate model, teams can swap the LLM, voice or language.

How accurate is Voxtral Transcribe 3?

These numbers are self-reported by Mistral:

FLEURS 45 (WER, forced / inferred language): Small 4.1% / 4.0%. MAI-Transcribe-2 ties at 4.1% / 4.0%. Mini 4.6% / 4.5%. Whisper large-v3 16.0% / 14.9%.
Diarization (cpWER, 5 public sets): Small 18.4% and Mini 18.6%. Pyannote scores 19.0% and MAI-Transcribe-2 20.3%.
Keyphrase recall (Earnings 22): Small 94.6% with context, 81.4% without. Scribe v2 posts 94.3% and 84.0%.

On Artificial Analysis, Small scores 2.1% AA-WER and Mini 2.2%. Only Fun-Realtime-ASR-preview (1.7%) and MAI-Transcribe-2 (2.0%) rank higher. Speed is the gap: Small’s speed factor is 65.8x, against 307.5x for MAI-Transcribe-2.

How do realtime transcription and TTS compare?

On FLEURS 35, the realtime model trades latency for accuracy. It scores 5.7% WER at 2,400 ms and 6.4% at 480 ms. At 240 ms, WER rises to 7.4%. Gemini 3.5 Realtime Transcribe scores 4.2% and MAI-Transcribe-2 Streaming 5.8%. GPT Live Transcribe (8.4%) and Scribe v2 Realtime (8.7%) trail every Voxtral setting.

Voxtral Speech 3 costs $13 per 1M characters. Mistral says that undercuts OpenAI TTS-1, Deepgram, ElevenLabs Flash and Gemini Flash. Explicit controls cover

emotion

pacing

and

emphasis

. The model page lists ~90 ms time-to-first-audio and 34 emotion tags. Pro Voice adapts a speaker’s style from 30 minutes of audio. Artificial Analysis places it in the top 12 of both TTS arenas.

Voxtral 3 vs MAI-Transcribe-2, Scribe v2 and Gemini 3.5 Transcribe

	Voxtral Small Transcribe 3	MAI-Transcribe-2	ElevenLabs Scribe v2	Gemini 3.5 Transcribe
Developer	Mistral AI	Microsoft AI	ElevenLabs	Google
Parameters	Not disclosed (Mini: 4B)	Not disclosed	Not disclosed	Not disclosed
Languages	50	60	90+	85+
FLEURS 45 WER, forced / inferred (Mistral)	4.1% / 4.0%	4.1% / 4.0%	4.9% / 4.9%	6.1% / 4.5%
Diarization cpWER (Mistral)	18.4%	20.3%	23.0%	38.4%
AA-WER (Artificial Analysis)	2.1%	2.0%	2.2%	2.6%
AA speed factor (Artificial Analysis)	65.8x	307.5x	65.4x	89.5x
Price per 1,000 min (Artificial Analysis)	$4.00	$1.67	$3.67	$5.00
Vendor list price	$0.004/min	$0.10/hour, limited-time offer	$0.22/hour	Token-billed, see Google pricing

How much does Voxtral 3 cost, and where can you run it?

Per Mistral’s price table:

Voxtral Mini Transcribe 3 (batch): $0.0035/min
Voxtral Small Transcribe 3: $0.004/min
Voxtral Mini Transcribe Realtime 3: $0.008/min
Voxtral Speech 3 (batch and realtime): $13 per 1M characters
Voice agent API: $0.04/min with Mistral Small 4, $0.08/min with any other reasoning model

The models run on Mistral’s European, US and global endpoints. OpenRouter also lists all 3 new models with EU-hosted, zero-data-retention endpoints.

Key Takeaways

Voxtral 3 bundles 3 ASR models, 1 TTS model and a LiveKit voice agent SDK.
Small Transcribe 3 hits 2.1% AA-WER, #3 on Artificial Analysis.
Best-in-chart diarization: 18.4% cpWER, ahead of Pyannote and MAI-Transcribe-2.
Batch transcription costs $0.0035 to $0.004 per minute; TTS costs $13 per 1M characters.
Realtime accuracy trails Gemini 3.5 Realtime, and no open weights were announced.

Check out the Voxtral 3 announcement, the audio docs and the model pages. All credit goes to the researcher of this project. Also,feel free to follow us on**Twitterand don’t forget to join our150k+ML SubRedditand Subscribe toour Newsletter. Wait! are you on telegram?now you can join us on telegram as well.**

Asif Razzaq

Website|+ posts Bio ⮌

Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.