Performance and Efficiency Gains
Microsoft's MAI-Transcribe-1.5 represents a significant iteration in their in-house speech-to-text stack, focusing on production-grade performance. The model achieves a 2.4% Word-Error-Rate (WER) on the Artificial Analysis leaderboard, positioning it as a competitive option for high-accuracy transcription.
Efficiency is the model's primary differentiator, particularly for long-form audio. Microsoft reports that the model is up to 5x faster than competitors like Gemini 3.1 and GPT-4o-Transcribe, and 5.7x faster than its predecessor, MAI-Transcribe-1. An hour of audio can now be processed in under 15 seconds, a critical improvement for batch-processing large archives.
Enterprise-Focused Features
Beyond raw speed, the model introduces features designed to solve common enterprise transcription failures:
- Entity Biasing: Users can provide up to 200 domain-specific keywords (names, medical terms, internal acronyms). The model uses contextual awareness to apply these biases, rather than forcing matches blindly. This has been shown to reduce WER by 30% on the FLEURS benchmark.
- Expanded Language Support: The model now supports 43 languages, up from 25. This includes 10 new South Asian languages and 8 European languages, all integrated into a single system.
- Automatic Language Identification: The model can now detect the input language without requiring manual configuration, simplifying deployment in global contact centers and multi-language meeting environments.