Microsoft’s MAI-Transcribe-2 claims 10x faster transcription than OpenAI

What MAI-Transcribe-2 offers

Microsoft has released MAI-Transcribe-2, a speech-recognition model designed to convert audio into text faster than existing tools. According to YourStory, the model is reportedly 10 times faster than OpenAI’s GPT-Transcribe, 5 times faster than Gemini 3.5 Transcribe, and 7 times faster than Scribe v2.

The model supports transcription in 60 languages, an increase from the 43 languages supported by its predecessor. It is built to handle real-world audio conditions, including background noise, poor-quality recordings, and conversations with multiple speakers.

Features for business use

MAI-Transcribe-2 includes features tailored for enterprise and business applications. Speaker diarisation identifies individual speakers in a recording, while word-level timestamps attach timing information to each word, simplifying tasks like caption editing or locating specific sections of audio.

Keyword biasing allows users to guide the model toward specialised terminology, which could be useful in fields like healthcare, legal services, and customer support. The model also offers automatic language identification, eliminating the need for manual language selection.

Pricing and availability

Microsoft is offering MAI-Transcribe-2 at a limited-time price of $0.10 per hour of audio through the end of 2026. This is a significant reduction from the $0.36 per hour pricing of the earlier model, which was released five months prior.

For businesses processing large volumes of audio, the lower pricing could result in substantial cost savings. YourStory notes that a company transcribing 100,000 hours of call-centre audio annually would pay $10,000 at the new rate, compared to $36,000 under the previous pricing.

The model is available through Microsoft Foundry and MAI Playground.

Accuracy and performance

Microsoft claims MAI-Transcribe-2 ranks first on the FLEURS benchmark for accuracy across 60 languages, with an average word error rate (WER) of 5.2%. A lower WER indicates better accuracy, as it measures how often the transcription system misidentifies words.

The company also reports that the model ranks second on Artificial Analysis’ word-error-rate leaderboard and is positioned on the accuracy-latency Pareto frontier, aiming to balance transcription quality with processing speed.

What this means

Lazyfounder analysis — our interpretation, not reported fact.

MAI-Transcribe-2’s speed and pricing could pressure competitors in the transcription space to accelerate their own product roadmaps or adjust pricing. For startups and businesses reliant on transcription—particularly those in call centres, media, or healthcare—the model’s features and cost structure may offer a compelling alternative to existing solutions. However, its real-world performance in non-English languages, local accents, and noisy environments will determine its adoption beyond controlled benchmarks.

Key takeaways

• Startups and businesses processing large volumes of audio could see significant cost savings with MAI-Transcribe-2’s pricing.
• The model’s focus on real-world conditions—such as background noise and multi-speaker conversations—may address practical challenges for call centres, media, and healthcare.
• Accuracy claims and features like keyword biasing could make it a viable option for industries requiring specialised terminology, such as legal, healthcare, and customer support.
• Indian startups and SaaS companies may need to evaluate its performance with local accents and mixed-language conversations.