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  4. Mistral AI launches Voxtral 3 voice agent system
Models & Releases30Funding & Deals17Benchmarks15Agents & Products50Hardware & Compute14Policy & Drama32Robotics12
Models & Releases

Mistral AI launches Voxtral 3 voice agent system

The Decoder: Mistral AI launches Voxtral 3, a full voice agent stack that listens, thinks, and talks in real time

Mistral AI launched Voxtral 3, a new family of language models built for real-time voice agents. The system combines speech-to-text, text-to-speech, and a voice agent SDK, supports more than 50 languages, and lets agents tap into external tools during live conversations. It can run on your own infrastructure or through EU-hosted APIs to keep data under your control. The article Mistral AI launches Voxtral 3, a full voice agent stack that listens, thinks, and talks in real time appeared

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A company has released a voice agent system that combines three components: a speech-to-text model, a text-to-speech model, and an agent framework that work together in real time to listen, process, and respond to users. The system supports transcription in more than 50 languages and text-to-speech in over 35 languages with 50 dialects and emotional adaptation. Agents can access external systems during conversations to perform tasks like booking appointments or checking invoices. The system can run on private infrastructure or through EU-hosted APIs, with transcription priced at $0.0035 per minute.

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Voxtral 3

Voxtral 3: Mistral's voice agent SDK for real-time speech in 50+ languages — accurate transcription, emotional TTS, deep tool use. EU-hosted or self-deployed.

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DeepSeek Open-Sources DeepInfer, the Inference Framework Behind DeepSeek-V4.1-Flash

DeepSeek has open-sourced DeepInfer, the inference framework that runs its production services. The DeepSeek DeepInfer inference framework is the engine behind DeepSeek-V4.1-Flash. It ships under the Apache 2.0 license. DeepSeek also released deepseek-recipe, its MIT-licensed token encoding and decoding layer. For developers, this is a rare look at a frontier lab’s real serving stack. TL;DR Size / scope: 1 supported model (DeepSeek-V4.1-Flash), 1M-token context window per the model

Also reported by The Decoder and SiliconANGLE

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Nvidia launches new open models to run agents in the enterprise and on PCs

Nvidia is leaning hard into the trend of enterprise companies wanting to run their own open models in their own data centers and also trying to make on-device, private AI agents a thing.

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Meta and Apple team up to make Muse AI models run better on Macs

The new Muse Spark Open AI model can work on M5 Ultra Mac Studios. | Photo: Amelia Holowaty Krales / The Verge Meta announced the Muse Spark Open AI model today, claiming that it's the "most capable US open-weight model to date." But the company also says that it's the "the first model in its class" that can "run on your desktop using two Apple M5 Ultra Mac Studios with the latest MLX framework." Meta and Apple, typically rivals, worked together on the launch and are publicly su

Also reported by The Decoder, TechCrunch, SiliconANGLE and MarkTechPost

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Elevenlabs releases two new speech-to-speech models designed for real-time AI conversations

Elevenlabs' new Eleven Realtime speech-to-speech models combine speed and accuracy. The v1 Max variant scores 98.8 percent on the Big Bench Audio benchmark with a 0.57-second response time. Elevenlabs says no model on the Artificial Analysis leaderboard is both faster and more accurate. The article Elevenlabs releases two new speech-to-speech models designed for real-time AI conversations appeared first on The Decoder.

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Cloudflare Releases Clairic, a System One Extraction Model on Workers AI

Cloudflare has released a preview of Clairic, a new System One extraction model on Workers AI. The Cloudflare Clairic extraction model reads text or images and returns typed values, not generated text. It targets a job developers usually give to LLMs: pulling totals, dates and IDs into JSON. Clairic answers every requested field in 1 forward pass, with no reasoning and no output tokens. TL;DR Size: Parameter count not disclosed. 65,536-token context window, up to 4 images per request.

Also reported by The Decoder