The Mistral.AI team introduces on a paper published on February 11, 2026 Voxtral Realtime, a newly developed speech recognition model designed to provide streaming transcriptions with extremely low latency. Unlike traditional systems that process audio in chunks, this 4.4-billion parameter model is trained end-to-end to transcribe audio as it is recorded, supporting 13 different languages. It utilizes a causal audio encoder and a specialized Ada RMS-Norm mechanism to maintain high accuracy even at sub-second delays. At a lag of only 480 milliseconds, its performance rivals leading offline systems like Whisper. To support widespread use, the developers have released the open weights and integrated the technology into the vLLM framework for efficient live serving. This innovation demonstrates that real-time AI can achieve the same quality as non-instantaneous models without sacrificing speed or language coverage. Source: February 2026 Voxtral Realtime Mistral AI Alexander H. Liu, Andy Ehrenberg, Andy Lo, Chen-Yo Sun, Guillaume Lample, Jean-Malo Delignon, Khyathi Raghavi Chandu, Patrick von Platen, Pavankumar Reddy Muddireddy, Rohin Arora, Sanchit Gandhi, Sandeep Subramanian, Soham Ghosh, Srijan Mishra https://arxiv.org/pdf/2602.11298