Microsoft Corp. today expanded its MAI artificial intelligence model family with its first streaming transcription model, debuting alongside two others focused on text-to-speech.
They’re designed for developers who want to build voice agents that can listen to people’s voices and reply instantly, similar to how humans talk to one another.
The most consequential of the three is MAI-Transcribe-2-Streaming, which the company said accepts human speech via a WebSocket, transforming it into a transcript that’s continuously updated as the person keeps on talking. Once the person has finished speaking, it will confirm that the transcript is final. In this way, the model can power applications that display live captions or start processing a user’s request before they’ve finished saying it, Microsoft said.
MAI-Transcribe-2-Streaming is listed on Microsoft’s Vercel AI Gateway and priced at 54 cents per audio hour, the company said. It supports more than 60 languages, and can detect which one someone is speaking automatically. It delivers its first transcript hypotheses within 320 milliseconds on average, although Microsoft said it cannot guarantee this kind of performance in every scenario, because speed of response is also determined by the network connection and the AI system that generates the reply.
Introducing 3 new models: MAI-Transcribe-2-Streaming, MAI-Voice-2.1 and MAI-Voice-2.1-Flash.
Accurate streaming transcription. Natural speech and less waiting between turns.
Build voice agents that keep the conversation moving! pic.twitter.com/QB6Yr9UiyK
— Microsoft AI (@MicrosoftAI) October 1, 2026
Microsoft isn’t new to transcription models. Last month, it debuted the MAI-Transcribe-2 model with a price of 10 cents per audio hour, which is more than five times cheaper than the Streaming variant. That’s mainly because the Streaming model does a lot more processing, continuously returning provisional results while the audio is still arriving. The regular MAI-Transcribe-2 model simply waits for the speaker to finish what they’re saying before processing begins.
Moving away from transcription, there are two models focused on generating speech from text, which are equally important for conversational AI agents. They include MAI-Voice-2.1, which is said to be the best choice for more expressive and higher-fidelity outputs, and MAI-Voice-2.1-Flash, which tones down those attributes in favor of a speedier response and lower cost.
According to the Vercel AI Gateway listings, MAI-Voice-2.1 is priced at $22 per million characters, while the Flash version costs $15 per million characters. Both of the text-to-speech models support 23 languages, Microsoft said.
The new releases demonstrate that Microsoft is accelerating its transition away from model providers such as OpenAI Group PBC and Anthropic PBC, despite being a major investor in both of those companies. In July, it was reported that Microsoft AI Chief Executive Mustafa Suleyman was becoming increasingly concerned about the costs associated with OpenAI’s and Anthropic’s powerful frontier models.
In response, he instructed Microsoft’s AI researchers to double down on the MAI model family, with the goal of eventually using those models to power its Copilot agents in platforms such as Excel and Outlook. “We pay a lot of money to Anthropic, so our goal is to reduce and ultimately eliminate that cost,” Suleyman told Bloomberg in an interview.
With the launch of the new models, Microsoft has everything it needs to build powerful voice AI agents that can converse with their human users in a natural, humanlike way. Voice agents need to be able to do three things to function: They must be able to recognize and understand what someone is saying to them, then decide what to do based on what was said, and finally generate an audible response.
MAI-Transcribe-2-Streaming takes care of the first problem, and the MAI-Voice models solve the third. What sits in the middle is Microsoft’s powerful reasoning model, Mai-Thinking-1, which is a standard large language model that reads through the transcripts of what was said to decide what the agent should do. By using three separate models, developers have much more control over the quality, latency and costs of their voice agents.