April 2, 2026
MAI-Transcribe-1: Everything you need to know
Microsoft has released MAI-Transcribe-1: a speech transcription model achieving 3.0% on AA-WER (#4), and is fast at 69x realtime
The model was developed by Microsoft AI (MAI)’s Superintelligence team and supports 25 languages including English, French, Arabic, Japanese, and Chinese. MAI-Transcribe-1 API is currently available in public preview via Azure Speech on Microsoft Foundry.
On the Artificial Analysis Speech to Text (STT) leaderboard, MAI-Transcribe-1 achieves a 3.0% word error rate on AA-WER for speech transcription accuracy, positioning it 4th overall behind Mistral’s Voxtral Small (2.9% AA-WER), Google’s Gemini 3.1 Pro High (2.9% AA-WER) and ElevenLabs’ Scribe v2 (2.3% AA-WER). It also stands out as one of the faster high-accuracy transcription models available, processing audio at ~69x real-time.

On speed, MAI-Transcribe-1 transcribes approximately 69 seconds of audio per second of processing, making it the fastest model in the top 5 by accuracy.

MAI-Transcribe-1 is available at $6 per 1000 minutes of audio via Microsoft Foundry.

See full results on the Artificial Analysis Speech to Text leaderboard: https://artificialanalysis.ai/speech-to-text
Read the latest

Artificial Analysis Decision Index v1.0
The Artificial Analysis Decision Index benchmarks decision models, a new category of models that answer structured decisions in a single call with a probability for each option. Version 1.0 combines four evaluations across classification, tool calling, knowledge, hallucination, and reasoning.
October 13, 2026

Announcing AA-3D: a benchmark for AI models building interactive 3D experiences
AA-3D is a new benchmark of how well AI models build interactive 3D experiences. Each model turns a written brief into a working three.js scene in an offline sandbox, and our recruited panel compares the scenes in blind pairwise votes.
October 12, 2026

Announcing FrontierCode on Artificial Analysis: can AI models write code maintainers would merge?
Cognition's FrontierCode 1.1 tests whether AI models make code changes that maintainers of real open source repositories would merge. Leading models still fail more than 40% of the time.
October 12, 2026