Microsoft launches MAI-Transcribe-2: transcription at $0.10 an hour

By Carlos Montiel | Enterprise AI Specialist
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Published: 2026-09-04 | By: Carlos Montiel | Reading time: ~4 minutes

Microsoft AI unveiled MAI-Transcribe-2 this week, launching at $0.10 per hour of audio — a fraction of what today's transcription models from OpenAI, Google, and ElevenLabs cost — and describes it as the fastest, most accurate, and cheapest speech recognition model available.

The price, in context

$0.10 per hour of audio works out to roughly $1.67 per 1,000 minutes. That's a steep drop even against its own predecessor: MAI-Transcribe-1 launched at $0.36 an hour. Microsoft confirms this is a promotional price valid through December 31, 2026 — it hasn't yet revealed what the regular price will be after that date, so it's worth not building a long-term budget around this rate holding indefinitely.

The comparison Microsoft is making

Microsoft positions MAI-Transcribe-2 explicitly above OpenAI's and Google's alternatives on speed and accuracy, in addition to price — the company describes it as "the fastest, most accurate, and cheapest speech recognition model in the world" in its own announcement. As with any benchmark published by the manufacturer itself, it's worth validating with real audio from your own use case (accents, background noise, specific technical terminology) before migrating a production pipeline based solely on the marketing figure.

Who this matters for: any company that today transcribes support calls, meetings, video content, or call center audio at scale. If your volume is in the thousands of hours a month, the difference between $0.36 and $0.10 an hour stops being marginal and becomes a budget line worth reviewing directly.

What it means for enterprise AI architectures

A cheaper, faster transcription component lowers the total cost of any pipeline that depends on converting speech to text before passing it to an LLM — for example, an agent that summarizes sales calls, a sentiment-analysis system over support recordings, or a compliance flow that transcribes and audits regulated communications. As with any piece of AI infrastructure, the practical recommendation is to design the integration so the transcription provider is swappable (a common interface, not a hardcoded dependency on a specific SDK) — pricing and "best model" leadership in this space keep changing every few months.

Carlos Montiel
Enterprise AI Solutions Architect
Specialist in LLMs, Agents and Orchestration
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