Mistral releases Voxtral 3 speech models and a voice agent SDK covering 50+ languages

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Mistral releases Voxtral 3 speech models and a voice agent SDK covering 50+ languages

Mistral releases Voxtral 3, a family of speech-to-text and text-to-speech models plus a voice agent SDK that runs listening, reasoning, tool calling, and speaking in one real-time streaming pipeline.
Voxtral Small Transcribe 3 supports 50+ languages, including 24 official EU languages, and Mistral says it outperforms Whisper, Scribe v2 and Gemini 3.5 Transcribe on English, French and Spanish.
The Mini variant has 4B parameters and costs $0.0035/min, and both models support context biasing so domain vocabulary works without fine-tuning.
Voxtral Mini TTS 3 adds voices that adjust emotion, pace and emphasis to context, with support for 35+ languages and 50+ dialects.
Pro Voice reproduces a speaker's style across languages from just 30 minutes of audio, and the models are offered through a EU-hosted API or self-deployment.

Hacker News opinions

Closed model, so it's a hard pass for me. TTS and STT still feel rough for local deployments.

Read the "EU-hosted API or self-deployment" line twice and I still can't tell if the weights are downloadable.

Qwen ASR and QwenTTS are good and not too big. Kokoro is the best sounding local TTS I've heard, and it runs realtime on phones.

Speaches.ai is my pick. Not for the models, but the package is easy to set up and latency was lower than I expected.

How real time is this? They're showing zero lag, and the explanation says they target under 1 second.

I think the real trick isn't the STT/TTS quality. It's wiring a real-time reasoning model to the raw conversation text and hiding tool call latency when it searches.

I've found the Voxtral transcription models solid for podcasts. Good diarization, and the older ones didn't hallucinate text when there was no speech.

The voice examples aren't great. Version 1 sounded like real people, and this one sounds like generic AI slop.

Scale is the answer for YouTube captions. About 500 hours of video get uploaded every minute, so even Google can't run the biggest model on all of it.

YouTube captions also miss the key word. "Rust" turns into "rats" because the rare word is exactly what the model guesses wrong.